{"id":47953,"date":"2026-10-23T08:26:00","date_gmt":"2026-10-23T08:26:00","guid":{"rendered":"https:\/\/dpc-software.de\/?page_id=47953"},"modified":"2026-11-02T09:11:46","modified_gmt":"2026-11-02T09:11:46","slug":"german-stata-conference-2026","status":"publish","type":"page","link":"https:\/\/dpc-software.de\/en\/german-stata-conference-2026\/","title":{"rendered":"German Stata Conference 2026"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"47953\" class=\"elementor elementor-47953\" data-elementor-post-type=\"page\">\n\t\t\t\t<div class=\"elementor-element elementor-element-11ba9053 e-con-full e-flex e-con e-child\" data-id=\"11ba9053\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-2dd02e50 e-flex e-con-boxed e-con e-child\" data-id=\"2dd02e50\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-62c4d404 elementor-widget elementor-widget-heading\" data-id=\"62c4d404\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">German Stata Conference 2026<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51e3b7da elementor-widget elementor-widget-spacer\" data-id=\"51e3b7da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e24bae9 e-flex e-con-boxed e-con e-parent\" data-id=\"e24bae9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e9ac3a2 elementor-widget elementor-widget-heading\" data-id=\"e9ac3a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Report German Stata Conference 2026<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2e62d68 elementor-widget elementor-widget-text-editor\" data-id=\"2e62d68\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>On June 18\u201319, 2026, the 23rd German Stata Conference brought the international Stata community together in Munich for two inspiring days of learning, exchange, and innovation. Researchers, data analysts, and professionals from academia, industry, and the public sector gathered to explore the latest developments in statistical analysis and data visualization with Stata.\u00a0\u00a0<\/p><p>With a strong focus on practical insights, methodological innovation, and networking, this year\u2019s conference once again\u00a0demonstrated\u00a0why the German Stata Conference\u00a0remains\u00a0a key event for anyone working with data.\u00a0<\/p><p><strong>Workshop Highlight: Visualization with Stata<\/strong><\/p><p>The event kicked off with a full-day workshop led by Dr. Christian Brzinsky-Fay, focusing on one of the most relevant topics in modern data analysis: effective data visualization.\u00a0<\/p><p>Participants explored how to turn complex analytical results into clear, compelling visuals.\u00a0The\u00a0hands-on\u00a0workshop\u00a0provided\u00a0practical\u00a0techniques\u00a0for:\u00a0<\/p><ul><li>Creating publication-ready graphics in Stata<\/li><li>Using AI Tools to Create Graphics<\/li><li>Improving standard charts for better readability\u00a0<\/li><li>Avoiding common pitfalls in data presentation\u00a0<\/li><li>Communicating\u00a0complex\u00a0findings\u00a0more\u00a0effectively\u00a0<\/li><\/ul><p>The interactive format encouraged discussion, experimentation, and direct application\u2014equipping participants with actionable skills they can\u00a0immediately\u00a0use in their daily work.\u00a0\u00a0<\/p><p><strong>Keynote Highlight: Visualizing Relationships in Stata\u00a0<\/strong><\/p><p>A true highlight of the conference was the keynote by Dr. Asjad Naqvi (Austrian Institute for Economic Research).\u00a0\u00a0<\/p><p>In his talk\u00a0<em>\u201cVisualizing relationships in Stata\u201d<\/em>,\u00a0he addressed one of the key challenges in modern data science: making complex relational data understandable and insightful.\u00a0<\/p><p>Using real-world examples such as trade flows, financial transactions, and migration data, Dr. Naqvi\u00a0demonstrated\u00a0how advanced visualization techniques can:\u00a0<\/p><ul><li>Reveal hidden structures and patterns in data<\/li><li>Highlight concentration and asymmetries<\/li><li>Provide deeper insights into network relationships<\/li><li>Transform complex datasets into intuitive visual stories\u00a0<\/li><\/ul><p>His presentation showed impressively how powerful visualization can bridge the gap between data and decision-making\u2014turning analysis into impact.\u00a0\u00a0<\/p><p><strong>A Diverse and High-Quality Program\u00a0<\/strong><\/p><p>Beyond the keynotes, the conference offered a rich program of presentations covering:\u00a0<\/p><ul><li>Methodological advances and new Stata commands<\/li><li>Practical applications from empirical research<\/li><li>Innovative approaches to data analysis and modeling\u00a0<\/li><\/ul><p>This diversity\u00a0ensured\u00a0valuable takeaways for both experienced users and newcomers\u2014making the conference a dynamic and engaging experience throughout the day.\u00a0<\/p><p><strong>More Than Just Content: A Strong Community\u00a0<\/strong><\/p><p>What makes the German Stata Conference special is not only the content\u2014but the community behind it.\u00a0<\/p><p>Participants exchanged ideas, discussed challenges, and built new connections across disciplines and countries, reinforcing the strong and growing Stata network.<\/p><p><strong>Conclusion: Practical, Inspiring, Future-Oriented<\/strong><\/p><p>The 23rd German Stata Conference 2026 was\u00a0a great success\u2014combining:\u00a0<\/p><ul><li>Hands-on learning<\/li><li>High-level expertise<\/li><li>International exchange<\/li><li>Immediate\u00a0practical\u00a0relevance\u00a0<\/li><\/ul><p>A big thank you to all speakers, participants, and partners who made this event possible.\u00a0<\/p><p>We look forward to seeing you at the next German Stata Conference!\u00a0\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e315ad4 e-flex e-con-boxed e-con e-parent\" data-id=\"e315ad4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a1d04da elementor-widget-tablet__width-inherit elementor-widget elementor-widget-the7-heading\" data-id=\"a1d04da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"the7-heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-heading-title elementor-size-default\">Keynote Speakers<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b7a5c09 e-grid e-con-boxed e-con e-parent\" data-id=\"b7a5c09\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-39d59295 e-con-full e-flex e-con e-child\" data-id=\"39d59295\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b3c2ba5 content-align-center the7-hover-visibility-disabled preserve-img-ratio-cover transform-overlay-yes exceeding-frame-yes elementor-widget elementor-widget-the7-image-widget\" data-id=\"b3c2ba5\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;hover_visibility&quot;:&quot;disabled&quot;}\" data-widget_type=\"the7-image-widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-image-container\"><div class=\"the7-image-wrapper the7-elementor-widget the7-transform-container\"><div class=\"post-thumbnail-rollover layzr-bg img-css-resize-wrapper\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1706\" height=\"2250\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#039;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#039;%20viewBox%3D&#039;0%200%201706%202250&#039;%2F%3E\" class=\"attachment-full size-full preload-me aspect lazy lazy-load\" alt=\"\" sizes=\"(max-width: 1706px) 100vw, 1706px\" style=\"--ratio: 1706 \/ 2250;\" data-src=\"https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi.png\" loading=\"eager\" data-srcset=\"https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi.png 1706w, https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi-303x400.png 303w, https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi-776x1024.png 776w, https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi-768x1013.png 768w, https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi-1165x1536.png 1165w, https:\/\/dpc-software.de\/wp-content\/uploads\/profile_pic_A_Naqvi-1553x2048.png 1553w\" \/><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-61865cc2 e-con-full e-flex e-con e-child\" data-id=\"61865cc2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7d5bce47 elementor-widget-tablet__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"7d5bce47\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Dr. Asjad Naqvi<\/strong><br \/>(Austrian Institute for Economic Research) presents: <em>Visualizing relationships in Stata<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5ce8441 e-con-full e-flex e-con e-child\" data-id=\"5ce8441\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e8fe4c content-align-right the7-hover-visibility-disabled preserve-img-ratio-cover transform-overlay-yes exceeding-frame-yes elementor-widget elementor-widget-the7-image-widget\" data-id=\"1e8fe4c\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;hover_visibility&quot;:&quot;disabled&quot;}\" data-widget_type=\"the7-image-widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-image-container\"><div class=\"the7-image-wrapper the7-elementor-widget the7-transform-container\"><div class=\"post-thumbnail-rollover layzr-bg img-css-resize-wrapper\"><img decoding=\"async\" width=\"2048\" height=\"1365\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#039;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#039;%20viewBox%3D&#039;0%200%202048%201365&#039;%2F%3E\" class=\"attachment-full size-full preload-me aspect lazy lazy-load\" alt=\"\" sizes=\"(max-width: 2048px) 100vw, 2048px\" style=\"--ratio: 2048 \/ 1365;\" data-src=\"https:\/\/dpc-software.de\/wp-content\/uploads\/van-kerm-1.jpg\" loading=\"eager\" data-srcset=\"https:\/\/dpc-software.de\/wp-content\/uploads\/van-kerm-1.jpg 2048w, https:\/\/dpc-software.de\/wp-content\/uploads\/van-kerm-1-400x267.jpg 400w, https:\/\/dpc-software.de\/wp-content\/uploads\/van-kerm-1-1024x683.jpg 1024w, https:\/\/dpc-software.de\/wp-content\/uploads\/van-kerm-1-768x512.jpg 768w, https:\/\/dpc-software.de\/wp-content\/uploads\/van-kerm-1-1536x1024.jpg 1536w\" \/><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-57fa9e2 e-con-full e-flex e-con e-child\" data-id=\"57fa9e2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7507298 elementor-widget-tablet__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"7507298\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Philippe van Kerm <br \/><\/strong>(University of Luxembourg) presents: <em>Shapley value calculations: Implementation and illustrations<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0b9e9b2 e-con-full e-flex e-con e-child\" data-id=\"0b9e9b2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b5f3dd3 content-align-right the7-hover-visibility-disabled preserve-img-ratio-cover transform-overlay-yes exceeding-frame-yes elementor-widget elementor-widget-the7-image-widget\" data-id=\"b5f3dd3\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;hover_visibility&quot;:&quot;disabled&quot;}\" data-widget_type=\"the7-image-widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-image-container\"><div class=\"the7-image-wrapper the7-elementor-widget the7-transform-container\"><div class=\"post-thumbnail-rollover layzr-bg img-css-resize-wrapper\"><img decoding=\"async\" width=\"2560\" height=\"1707\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#039;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#039;%20viewBox%3D&#039;0%200%202560%201707&#039;%2F%3E\" class=\"attachment-full size-full preload-me aspect lazy lazy-load\" alt=\"\" sizes=\"(max-width: 2560px) 100vw, 2560px\" style=\"--ratio: 2560 \/ 1707;\" data-src=\"https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-scaled.jpg\" loading=\"eager\" data-srcset=\"https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-scaled.jpg 2560w, https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-400x267.jpg 400w, https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-1024x683.jpg 1024w, https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-768x512.jpg 768w, https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-1536x1024.jpg 1536w, https:\/\/dpc-software.de\/wp-content\/uploads\/mkc-1-2048x1365.jpg 2048w\" \/><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0e2892c e-con-full e-flex e-con e-child\" data-id=\"0e2892c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-54a6527 elementor-widget-tablet__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"54a6527\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Meghan Cain<\/strong><br \/>(StataCorp LLC.) presents: <em>Psychometric meta-analysis<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6097733 e-flex e-con-boxed e-con e-parent\" data-id=\"6097733\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-40a2cfa elementor-widget elementor-widget-the7-heading\" data-id=\"40a2cfa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"the7-heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-heading-title elementor-size-default\">Workshop:  Workshop Visualization with Stata<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57d64b9 elementor-widget-tablet__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"57d64b9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The importance of visual communication increases not only on social media but also in research. It is therefore crucial to encode your results in graphs and diagrams in an appropriate way that allows readers a correct decoding of information. Stata provides publication-ready graphs with an endless number of modifications. Unfortunately, preparing scientific publications is usually a time-critical process, and researchers often don\u2019t have enough time to invest in perfect graphs.<\/p><p>The workshop addresses students and scientists of different levels of knowledge. We will deal with issues of efficient graph construction, i.e. how to systematically create colourful graphs that can be intuitively understood by readers. We will also talk about graph manipulation and a couple of tricks which are not part of the documentation. During the workshop, I will also introduce some helpful user-written ados with respect to graph construction.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c4b5855 elementor-widget elementor-widget-heading\" data-id=\"c4b5855\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Presenter\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c190ae6 e-con-full e-flex e-con e-child\" data-id=\"c190ae6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-e6160ce e-con-full e-flex e-con e-child\" data-id=\"e6160ce\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ec69555 content-align-right the7-hover-visibility-disabled preserve-img-ratio-cover transform-overlay-yes exceeding-frame-yes elementor-widget elementor-widget-the7-image-widget\" data-id=\"ec69555\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;hover_visibility&quot;:&quot;disabled&quot;}\" data-widget_type=\"the7-image-widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-image-container\"><div class=\"the7-image-wrapper the7-elementor-widget the7-transform-container\"><div class=\"post-thumbnail-rollover layzr-bg img-css-resize-wrapper\"><img decoding=\"async\" width=\"1706\" height=\"2250\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#039;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#039;%20viewBox%3D&#039;0%200%201706%202250&#039;%2F%3E\" class=\"attachment-full size-full preload-me aspect lazy lazy-load\" alt=\"\" sizes=\"(max-width: 1706px) 100vw, 1706px\" style=\"--ratio: 1706 \/ 2250;\" data-src=\"https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay.png\" loading=\"eager\" data-srcset=\"https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay.png 1706w, https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay-303x400.png 303w, https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay-776x1024.png 776w, https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay-768x1013.png 768w, https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay-1165x1536.png 1165w, https:\/\/dpc-software.de\/wp-content\/uploads\/brzinsky-fay-1553x2048.png 1553w\" \/><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7fd34f4 e-con-full e-flex e-con e-child\" data-id=\"7fd34f4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e85b70d elementor-widget elementor-widget-heading\" data-id=\"e85b70d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Dr. Christian Brzinsky-Fay\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d441e9f elementor-widget-tablet__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"d441e9f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Dr Christian Brzinsky-Fay has been working for DPC Software since March 2022 and puts his heart and soul into running Stata training courses. His view of the world was largely shaped by Monty Python. He studied Political Science at the Free University of Berlin and holds a PhD in Social Policy from the University of Tampere (Finland). As a quantitative social researcher, he has been using Stata for statistical analyses for 25 years and teaches statistics and empirical methods of social research at the University of Hamburg.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6a64e7a0 e-flex e-con-boxed e-con e-parent\" data-id=\"6a64e7a0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-82986cc elementor-widget-tablet__width-inherit elementor-widget elementor-widget-heading\" data-id=\"82986cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Lectures<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4daf6ccf elementor-widget__width-inherit elementor-widget elementor-widget-toggle\" data-id=\"4daf6ccf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"toggle.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1301\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-1301\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Visualizing relationships in Stata | Asjad Naqvi<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1301\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-1301\"><p><strong>Asjad Naqvi (Austrian Institute for Economic Research)<\/strong><\/p><p><em>Abstract:<\/em> When working with flow data in Stata, such as trade, financial transactions, or migration, we often encounter limitations in visualizing network structures. To address these challenges, this presentation introduces and showcases a set of tools for working with relational data in Stata. These tools can enable users to visualize and communicate metrics such as concentration, asymmetries, and other structural patterns embedded in flow relationships.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Stata_Munich_2026-Asjad.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1302\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-1302\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">drlate: Doubly Robust and Covariate-Balancing Estimation of LATE in Stata | Derya Uysal<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1302\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-1302\"><p><strong>Derya Uysal (LMU Munich), Tymon Sloczy\u0144ski (Brandeis University), and Jeffrey M. Wooldridge (Michigan State University)<\/strong><\/p><p><em>Abstract:<\/em> We introduce drlate, a new Stata command for estimating local average treat ment effects (LATE) and local average treatment effects for the treated (LATT) using doubly robust and covariate- balancing methods. The command complements Stata\u2019s lateffects by expanding the set of available estimators and improving inference.<br \/>drlate implements regression adjustment, inverse probability weighting (IPW), IPWRA, AIPW, and normalized versions of IPW and AIPW estimators. Outcomes may be continuous, binary, or count. The treatment is binary (with extensions to continuous treatments under development), and the instrument is binary.<br \/>The instrument propensity score can be estimated either by maximum likelihood or by method-of-moments approaches that directly balance covariates. We implement covariate balancing propensity scores (Imai and Ratkovic, 2014) and inverse probability tilting (Graham, Pinto, and Egel, 2012, 2016) as covariate-balancing alternatives to likelihood based estimation.<br \/>In addition, we provide testing procedures for equality of LATE and LATT and for comparisons between LATE and both linear and nonlinear IV estimators. We also address an issue in the standard error calculation of lateffects and provide consistent variance estimation for all implemented estimators.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/drlate_stata_germany26.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1303\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-1303\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">classify: Over two hundred measures of association, correlation and forecast accuracy for categorical outcomes | Andrei Sirchenko<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1303\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-1303\"><p><strong>Andrei Sirchenko (Nyenrode Business University)<\/strong><\/p><p><em>Abstract:<\/em> We describe a new Stata command classify that computes various measures of association and correlation between two categorical variables (dichotomous and polytomous, nominal and ordinal), diagnostic scores of probabilistic forecasts of such variables, and various measures of the accuracy of deterministic forecasts of them. We compiled a comprehensive catalogue of over 200 measures of association, correlation and forecast verification and diagnostic scores for probabilistic forecasts from different fields, along with the terminological synonymy and bibliography associated with them. In addition to the overall measures, the command computes the class-specific metrics as well as their macro and weighted averages.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Presentation-Stata-2026-Classify-SIirchenko-1.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1304\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-1304\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Multilevel-Latent-Class Analysis with gsem | Wolfgang Langer<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1304\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-1304\"><p><strong>Wolfgang Langer (Martin-Luther-University Halle-Wittenberg)<\/strong><\/p><p><em>Abstract:<\/em> Since version 15 Stata offers the possibility to estimate Latent Class Models for categorical observed indicators being dichotomous, ordinal or nominal. It also integrates manifest covariates to predict the class membership of the observations. Both parts of the model, the measurement one and the prediction one, are estimated simultaneously, so that changes in the measurement part influence the estimates of the structural part and vice versa. According to Hayduk (1996) and Bakk &amp; Kuha (2020) I propose a 3 steps approach. Firstly I estimate a sequence of Latent Class Models identifying the most appropriate solution by the entropy criteria. Secondly I analyze profile plot of the item probabilities to attach meaningful labels to the latent classes. The assignment of observations to the discrete latent classes follows the highest probability rule. Thirdly I estimate a multinomial logit regression model to predict the discrete latent class membership by exogenous level 1 and level 2 variables. To estimate a logistic intercept-as-outcome model I use the Stata xtmlogit command introduced by version 17. I demonstrate the usefulness of this approach presenting a latent class analysis of attitudes towards vaccination at the eve of the Covid 19 pandemia using the Special Eurobarometer 488 data set. Being a vaccination supporter, a conspirator or a naif is predicted within 28 European countries by the personal characteristics of the respondents and between countries by their fixed effect dummy variables. To enlighten these black boxes I estimate a random-effect intercept-as-outcome multinomial logit model with the xtmlogit command using exogenous level 2 variables like collective level of trust in government, gdp, poverty rate and the Global Health Security Index in addition. Finally I discuss the<br \/>main results and give some methodological considerations.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Langer-2026-Multilevel-LCA-with-GSEM.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1305\" class=\"elementor-tab-title\" data-tab=\"5\" role=\"button\" aria-controls=\"elementor-tab-content-1305\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Shapley value calculations: Implementation and illustrations | Philippe van Kerm<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1305\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"region\" aria-labelledby=\"elementor-tab-title-1305\"><p><strong>Philippe van Kerm (University of Luxembourg)<\/strong><\/p><p><em>Abstract:<\/em>This presentation will introduce and illustrate the use of the Shapley\u2013Owen value in an array of applications. It will first introduce the concept of the Shapley value and the related concept of Owen value. It will then describe the \u2019shapowen\u2018 package, a generic calculator for the Shapley-Owen value. A range of examples will then show the practical implementation of the Shapley-Owen value in regression analysis and will illustrate a range of possible alternative uses of the Shapley-Owen drawn from income distribution research.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/20260619-gsug-vankerm.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong>\u00a0<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1306\" class=\"elementor-tab-title\" data-tab=\"6\" role=\"button\" aria-controls=\"elementor-tab-content-1306\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">crosswalk: A command for fast and flexible bulk recoding | Ben Jann<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1306\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"6\" role=\"region\" aria-labelledby=\"elementor-tab-title-1306\"><p><strong>Ben Jann (University of Bern)<\/strong><\/p><p><em>Abstract:<\/em> In this talk I will present the crosswalk command, a data management utility for fast table-based recoding. The command comes with predefined crosswalk tables for common recoding tasks related to occupational classifications, e.g. to translate ISCO codes (International Standard Classification of Occupations) into ISEI scores (International Socio-economic Index of Occupational Status), OEP scores (Occupational Earning Potential), or ESeC classes (European Socio-economic Classification). However, it is also easy to define, manipulate, and apply custom recoding tables. In the talk I will briefly explain how crosswalk is implemented, present its syntax, and then illustrate its use with some applied examples.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/crosswalk-Munich-2026-Ben-2.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1307\" class=\"elementor-tab-title\" data-tab=\"7\" role=\"button\" aria-controls=\"elementor-tab-content-1307\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Matching, weighting, or regression? Evidence from a comprehensive simulation study of Stata treatment effect estimators| Felix Bittmann<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1307\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"7\" role=\"region\" aria-labelledby=\"elementor-tab-title-1307\"><p><strong>Felix Bittmann (LIfBI, Bamberg)<\/strong><\/p><p><em>Abstract:<\/em> Estimating treatment effects with cross-sectional data is one of the most widespread approaches in empirical research. Provided that researchers are able to measure all relevant control variables, it is possible to approximate unbiased (causal) treatment effects. To this end, Stata offers a wide range of standard and user-written commands. Naturally, the question remains which of these methods is most robust for producing unbiased point estimates and valid inference. We address this question by evaluating 14 different commands in a comprehensive simulation study. Using four different settings (unbiased, biased, incorrect functional form, heterogeneous treatment effects), we analyze a variety of empirically relevant scenarios. Our results indicate that linear (OLS) regression exhibits the lowest bias, the smallest standard errors, and the most accurate coverage in almost all simulation specifications. Entropy balancing and some matching approaches offer advantages when nonlinearities are incorrectly specified. When heterogeneous treatment effects are present, regression adjustment or AIPW approaches deliver the best results. Surprisingly, several methods deviate substantially from the target estimands, even in unbiased \u201cbest-case\u201d scenarios.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Bittmann_Stata26.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1308\" class=\"elementor-tab-title\" data-tab=\"8\" role=\"button\" aria-controls=\"elementor-tab-content-1308\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">New functionality in blockops, a Mata library for efficient operations on block matrices | Daniel C. Schneider<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1308\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"8\" role=\"region\" aria-labelledby=\"elementor-tab-title-1308\"><p><strong>Daniel C. Schneider (MPIDR, Rostock)<\/strong><\/p><p><em>Abstract:<\/em> In Schneider (2025), a new Mata library called blockops was introduced. The library serves two main purposes. First, it provides a simple approach for working with a particular class of sparse matrices: Submatrices consisting entirely of zeros are represented by null pointers and are excluded from arithmetic operations. Second, it allows the application of built-in, (official) Mata library, or user-defined functions to each submatrix, in a manner similar to, for example, R\u2019s apply() family of functions. This presentation revisits the core ideas underlying blockops and then outlines recent developments. Key efficiency concerns have been addressed, and numerous new methods have been added to the library\u2019s central object, which represents a block matrix. The practical usefulness of the extended functionality is illustrated through several examples.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/stata_ugm26.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-1309\" class=\"elementor-tab-title\" data-tab=\"9\" role=\"button\" aria-controls=\"elementor-tab-content-1309\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-right\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><svg class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><svg class=\"elementor-toggle-icon-opened e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Psychometric meta-analysis | Meghan Cain<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1309\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"9\" role=\"region\" aria-labelledby=\"elementor-tab-title-1309\"><p><strong>Meghan Cain (StataCorp LLC.)<\/strong><\/p><p><em>Abstract:<\/em> This talk introduces meta psycorr, a new command in StataNow for psychometric meta-analysis. Psychometric meta-analysis provides a more rigorous framework than traditional meta-analysis by correcting for statistical artifacts\u2014including measurement error, range restriction, artificial dichotomization, and small-study bias. In this talk, we will introduce the theory and then demonstrate a practical Stata workflow. We will also generate corrected forest plots and explore heterogeneity using Stata\u2019s integrated meta suite.<\/p><p><strong><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Cain_2026.pdf\" target=\"_blank\" rel=\"noopener\">Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2eb5cd elementor-widget-tablet__width-inherit elementor-widget elementor-widget-heading\" data-id=\"a2eb5cd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/2026-German-Stata-Conference-Announcement-and-Program_compressed.pdf\">Download the full Program<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5f963dd e-flex e-con-boxed e-con e-parent\" data-id=\"5f963dd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f809ff9 elementor-widget elementor-widget-gallery\" data-id=\"f809ff9\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;gap&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:30,&quot;sizes&quot;:[]},&quot;lazyload&quot;:&quot;yes&quot;,&quot;gallery_layout&quot;:&quot;grid&quot;,&quot;columns&quot;:4,&quot;columns_tablet&quot;:2,&quot;columns_mobile&quot;:1,&quot;gap_widescreen&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;gap_laptop&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:10,&quot;sizes&quot;:[]},&quot;gap_tablet_extra&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:10,&quot;sizes&quot;:[]},&quot;gap_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:10,&quot;sizes&quot;:[]},&quot;gap_mobile_extra&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:10,&quot;sizes&quot;:[]},&quot;gap_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:10,&quot;sizes&quot;:[]},&quot;link_to&quot;:&quot;file&quot;,&quot;aspect_ratio&quot;:&quot;3:2&quot;,&quot;overlay_background&quot;:&quot;yes&quot;,&quot;content_hover_animation&quot;:&quot;fade-in&quot;}\" 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class=\"elementor-gallery-item__overlay\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<a class=\"e-gallery-item elementor-gallery-item elementor-animated-content\" href=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Medien-1.jpg\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-slideshow=\"f809ff9\" data-elementor-lightbox-title=\"Medien (1)\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NDc5NjYsInVybCI6Imh0dHBzOlwvXC9kcGMtc29mdHdhcmUuZGVcL3dwLWNvbnRlbnRcL3VwbG9hZHNcL01lZGllbi0xLmpwZyIsInNsaWRlc2hvdyI6ImY4MDlmZjkifQ%3D%3D\">\n\t\t\t\t\t<div class=\"e-gallery-image elementor-gallery-item__image\" data-thumbnail=\"https:\/\/dpc-software.de\/wp-content\/uploads\/Medien-1-300x400.jpg\" data-width=\"300\" data-height=\"400\" aria-label=\"\" role=\"img\" ><\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-gallery-item__overlay\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t<a class=\"e-gallery-item 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data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NDc5NjgsInVybCI6Imh0dHBzOlwvXC9kcGMtc29mdHdhcmUuZGVcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjYwNjE5XzExMTg0MS1zY2FsZWQuanBnIiwic2xpZGVzaG93IjoiZjgwOWZmOSJ9\">\n\t\t\t\t\t<div class=\"e-gallery-image elementor-gallery-item__image\" data-thumbnail=\"https:\/\/dpc-software.de\/wp-content\/uploads\/20260619_111841-400x300.jpg\" data-width=\"400\" data-height=\"300\" aria-label=\"\" role=\"img\" ><\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-gallery-item__overlay\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7b2c3f8 e-flex e-con-boxed e-con e-parent\" data-id=\"7b2c3f8\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a3d4d12 elementor-widget-tablet__width-inherit elementor-widget elementor-widget-the7-heading\" data-id=\"a3d4d12\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"the7-heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"the7-heading-title elementor-size-default\">Organizers<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e042bdf e-grid e-con-boxed e-con e-parent\" data-id=\"e042bdf\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-2032a15 e-con-full e-flex e-con e-child\" data-id=\"2032a15\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e5cddf3 elementor-widget elementor-widget-heading\" data-id=\"e5cddf3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Ulrich Kohler\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-906cfc8 elementor-widget elementor-widget-heading\" data-id=\"906cfc8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">University of Potsdam\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-522ce26 elementor-widget elementor-widget-text-editor\" data-id=\"522ce26\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"mailto:ulrich.kohler@uni-potsdam.de\" target=\"_blank\" rel=\"noopener\">ulrich.kohler@uni-potsdam.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-56903d0 e-con-full e-flex e-con e-child\" data-id=\"56903d0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-62fd9da elementor-widget elementor-widget-heading\" data-id=\"62fd9da\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Johannes Giesecke\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e617ac elementor-widget elementor-widget-heading\" data-id=\"6e617ac\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Humboldt University Berlin\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-22c4d51 elementor-widget elementor-widget-text-editor\" data-id=\"22c4d51\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"mailto:johannes.giesecke@hu-berlin.de\" target=\"_blank\" rel=\"noopener\">johannes.giesecke@hu-berlin.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2f38523 e-con-full e-flex e-con e-child\" data-id=\"2f38523\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9c2de2f elementor-widget elementor-widget-heading\" data-id=\"9c2de2f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Christian Ganser<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a208529 elementor-widget elementor-widget-heading\" data-id=\"a208529\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">LMU Munich\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-910a131 elementor-widget elementor-widget-text-editor\" data-id=\"910a131\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"mailto:christian.ganser@lmu.de\">christian.ganser@lmu.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3bd96c1 e-con-full e-flex e-con e-child\" data-id=\"3bd96c1\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3a97b5f elementor-widget elementor-widget-heading\" data-id=\"3a97b5f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Daniel Kr\u00e4hmer<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b60426 elementor-widget elementor-widget-heading\" data-id=\"0b60426\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">LMU Munich\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cfa9628 elementor-widget elementor-widget-text-editor\" data-id=\"cfa9628\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"mailto:reinhard.pollak@gesis.org\">daniel.kraehmer@soziologie.uni-muenchen.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>German Stata Conference 2026 Report German Stata Conference 2026 On June 18\u201319, 2026, the 23rd German Stata Conference brought the international Stata community together in Munich for two inspiring days of learning, exchange, and innovation. Researchers, data analysts, and professionals from academia, industry, and the public sector gathered to explore the latest developments in statistical&hellip;<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-47953","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>German Stata Conference 2026 | DPC Software GmbH<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dpc-software.de\/en\/german-stata-conference-2026\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"German Stata Conference 2026 | DPC Software GmbH\" \/>\n<meta property=\"og:description\" content=\"German Stata Conference 2026 Report German Stata Conference 2026 On June 18\u201319, 2026, the 23rd German Stata Conference brought the international Stata community together in Munich for two inspiring days of learning, exchange, and innovation. 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