{"id":4602,"date":"2024-10-08T21:32:05","date_gmt":"2024-10-08T21:32:05","guid":{"rendered":"https:\/\/www.stat.matf.bg.ac.rs\/?p=4602"},"modified":"2024-10-08T21:34:26","modified_gmt":"2024-10-08T21:34:26","slug":"%d1%81%d0%b5%d0%bc%d0%b8%d0%bd%d0%b0%d1%80-%d0%ba%d0%b0%d1%82%d0%b5%d0%b4%d1%80%d0%b5-%d0%b7%d0%b0-%d0%b2%d0%b5%d1%80%d0%be%d0%b2%d0%b0%d1%82%d0%bd%d0%be%d1%9b%d1%83-%d0%b8-%d1%81%d1%82%d0%b0%d1%82-18","status":"publish","type":"post","link":"https:\/\/www.stat.matf.bg.ac.rs\/en\/%d1%81%d0%b5%d0%bc%d0%b8%d0%bd%d0%b0%d1%80-%d0%ba%d0%b0%d1%82%d0%b5%d0%b4%d1%80%d0%b5-%d0%b7%d0%b0-%d0%b2%d0%b5%d1%80%d0%be%d0%b2%d0%b0%d1%82%d0%bd%d0%be%d1%9b%d1%83-%d0%b8-%d1%81%d1%82%d0%b0%d1%82-18\/","title":{"rendered":"Seminar of the Department of Probability and Statistics"},"content":{"rendered":"
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<\/p>\n

\u0423 \u0441\u0440\u0435\u0434\u0443 16. o\u043a\u0442\u043e\u0431\u0440\u0430 2024. \u0443 14:15 \u0447\u0430\u0441\u043e\u0432\u0430, \u0443 \u0441\u0430\u043b\u0438\u00a0 840, \u043f\u0440\u043e\u0444. \u0434\u0440 \u0410\u0440\u0442\u0435\u043c \u041f\u0440\u043e\u0445\u043e\u0440\u043e\u0432<\/a> (\u0423\u043d\u0438\u0432\u0435\u0440\u0437\u0438\u0442\u0435\u0442 \u0443 \u0421\u0438\u0434\u043d\u0435\u0458\u0443) \u045b\u0435 \u043e\u0434\u0440\u0436\u0430\u0442\u0438 \u043f\u0440\u0435\u0434\u0430\u0432\u0430\u045a\u0435<\/p>\n

CASUALLY ABOUT CAUSAL: NEYMAN ORTHOGONALITY AND M\/P REDUNDANCY, WITH APPLICATION TO PRODUCTION FRONTIER MODELS\u00a0<\/p>\n

\u0420\u0435\u0437\u0438\u043c\u0435<\/b>:\u00a0 Causal inference is about statistical testing for relationships, not about prediction. Machine learning is not so good at this. I will introduce the relatively new area of causal inference which adapts ML methods to the fundamental task of scientific discovery. Central to the methodology is the concept of Neyman orthogonal moment conditions. I will connect this concept with moment and parameter redundancy, a condition introduced by Prokhorov and Schmidt (2009) within General Method of Moments estimation, and I will show how this condition works out for a large class of models of production. The approach allows us to obtain robust post-ML inference of such important causal quantities as returns to scale and factor productivity.<\/p>\n

\u041b\u0438\u043d\u043a \u0437\u0430 \u043f\u0440\u0438\u0441\u0442\u0443\u043f \u0458\u0435<\/p>\n

Join Zoom Meeting
<\/a>https:\/\/zoom.us\/j\/95486885765?pwd=9qQB9k7yJ4V8wNOaBJzfp4UgaZkFay.1<\/a>

Meeting ID: 954 8688 5765
Passcode: 258075<\/a><\/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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

\u0423 \u0441\u0440\u0435\u0434\u0443 16. o\u043a\u0442\u043e\u0431\u0440\u0430 2024. \u0443 14:15 \u0447\u0430\u0441\u043e\u0432\u0430, \u0443 \u0441\u0430\u043b\u0438\u00a0 840, \u043f\u0440\u043e\u0444. \u0434\u0440 \u0410\u0440\u0442\u0435\u043c \u041f\u0440\u043e\u0445\u043e\u0440\u043e\u0432 (\u0423\u043d\u0438\u0432\u0435\u0440\u0437\u0438\u0442\u0435\u0442 \u0443 \u0421\u0438\u0434\u043d\u0435\u0458\u0443) \u045b\u0435 \u043e\u0434\u0440\u0436\u0430\u0442\u0438 \u043f\u0440\u0435\u0434\u0430\u0432\u0430\u045a\u0435 CASUALLY ABOUT CAUSAL: NEYMAN ORTHOGONALITY AND M\/P REDUNDANCY, WITH APPLICATION TO PRODUCTION FRONTIER MODELS\u00a0 \u0420\u0435\u0437\u0438\u043c\u0435:\u00a0 Causal inference is about statistical testing for relationships, not about prediction. Machine learning is not so good at this. […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4602","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/posts\/4602","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/comments?post=4602"}],"version-history":[{"count":4,"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/posts\/4602\/revisions"}],"predecessor-version":[{"id":4606,"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/posts\/4602\/revisions\/4606"}],"wp:attachment":[{"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/media?parent=4602"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/categories?post=4602"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.stat.matf.bg.ac.rs\/en\/wp-json\/wp\/v2\/tags?post=4602"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}