{"id":22858,"date":"2024-08-07T09:26:55","date_gmt":"2024-08-07T09:26:55","guid":{"rendered":"https:\/\/web.socsc.hku.hk\/event\/methods-hub-seminar-series-accessible-causal-inference\/"},"modified":"2024-08-07T09:26:55","modified_gmt":"2024-08-07T09:26:55","slug":"methods-hub-seminar-series-accessible-causal-inference","status":"publish","type":"event","link":"https:\/\/web.socsc.hku.hk\/tc\/event\/methods-hub-seminar-series-accessible-causal-inference\/","title":{"rendered":"Methods Hub Seminar Series: Accessible Causal Inference"},"content":{"rendered":"<p>Join us for the \u201cMethods Hub Seminar Series: Accessible Causal Inference\u201d featuring ????????? ?????? ???????! In this seminar, you&#8217;ll learn about the advantages of machine learning algorithms for identifying what works for whom and estimating causal effects for observational studies where treatment or intervention is not randomly assigned.<br \/>\nProfessor Perrett will also introduce new software to aid the implementation of these approaches. Don&#8217;t miss this opportunity to gain insights on causal inference methods for randomized and observational studies.<\/p>\n","protected":false},"author":1,"template":"","event_categories":[260],"event_tag":[859,867,240,242,860,267,868,861,239,241],"related_programme":[],"class_list":["post-22858","event","type-event","status-publish","hentry","event_categories-seminar","event_tag-hkusocialsciences","event_tag-causalinference","event_tag-foss","event_tag-hku","event_tag-hkuresearchhubofpopulationstudies","event_tag-methodshub","event_tag-observationaldata","event_tag-populationstudies","event_tag-socialsciences","event_tag-socientist"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2 - aioseo.com -->\n\t<meta name=\"description\" content=\"Join us for the \u201cMethods Hub Seminar Series: Accessible Causal Inference\u201d featuring ????????? ?????? ???????! In this seminar, you&#039;ll learn about the advantages of machine learning algorithms for identifying what works for whom and estimating causal effects for observational studies where treatment or intervention is not randomly assigned. 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