{"id":831499,"date":"2022-04-05T20:36:23","date_gmt":"2022-04-06T03:36:23","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=831499"},"modified":"2022-04-12T08:23:09","modified_gmt":"2022-04-12T15:23:09","slug":"2022-causal-inference-and-machine-learning-workshop","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/2022-causal-inference-and-machine-learning-workshop\/","title":{"rendered":"2022 Causal Inference and Machine Learning Workshop"},"content":{"rendered":"\n\n\n\n\n

Causal inference is one of the hotspots in data science and artificial intelligence research in recent years, and has received extensive attention from academia and industry. This workshop aims to further promote academic exchanges between researchers in the field of causal inference and machine learning, and explore the combination of causal inference and machine learning. This workshop is fortunate to invite 12 experts in related fields to give academic reports and conduct extensive academic discussions in related fields. The workshop will be held on April 2, 2022 in the lecture hall on the fourth floor of the Institute of Computing Technology, Chinese Academy of Sciences. <\/p>\n\n\n\n

<\/p>\n\n\n\n

Organizing Committee<\/h3>\n\n\n\n

Chairs:
<\/strong>Zhi-Ming Ma, Xueqi Cheng, Tie-Yan Liu<\/p>\n\n\n\n

Committee members:
<\/strong>Jiafeng Guo, Wei Chen, Changliang Zou, Chuan Zhou, Qi Meng, Ruqing Zhang, Lijun Sun<\/p>\n\n\n\n\n\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Date<\/th>\nTime<\/th>\nReporter<\/th>\nTitle<\/th>\nChair<\/th>\n<\/tr>\n<\/thead>\n
April 1<\/td>\n14:00-22:00<\/td>\nRegistration<\/td>\n<\/tr>\n
April 2<\/td>\n08:50-09:10<\/td>\nOpening ceremony<\/td>\nJiafeng Guo (CAS)<\/td>\n<\/tr>\n
09:10-09:40<\/td>\nHuazhen Lin\n\uff08Southwestern University of Finance and Economics\uff09\n<\/td>\nRobust and efficient estimation for treatment effect in causal inference<\/td>\nWei Chen\n(CAS)\n<\/td>\n<\/tr>\n
09:40-10:10<\/td>\nPeng Cui\n\uff08Tsinghua University\uff09\n<\/td>\nCausal-Inspired Stable Learning<\/td>\n<\/tr>\n
10:10-10:40<\/td>\nWei Lin\n\uff08Peking University\uff09\n<\/td>\nDeconfounding with the Blessing of Dimensionality<\/td>\n<\/tr>\n
10:40-11:00<\/td>\nCoffee Break<\/td>\n<\/tr>\n
11:00-11:30<\/td>\nWang Miao\n\uff08Peking University\uff09\n<\/td>\nCausal Inference, Observational Research and the Nobel Prize in Economics<\/td>\nChangliang Zou\n(NKU)\n<\/td>\n<\/tr>\n
11:30-12:00<\/td>\nRui Ding\n\uff08Microsoft\uff09\n<\/td>\nSupervised Causal Learning: A New Frontier of Causal Discovery<\/td>\n<\/tr>\n
12:00-12:30<\/td>\nLei Wang\n\uff08Nankai University\uff09\n<\/td>\nGeneralized regression estimators for average treatment effect with multicollinearity in high-dimensional covariates<\/td>\n<\/tr>\n
12:30-14:00<\/td>\nLunch<\/td>\n<\/tr>\n
14:00-14:30<\/td>\nWei Chen\n\uff08Microsoft\uff09\n<\/td>\nCombinatorial Causal Bandit<\/td>\nWang Miao\n(PKU)\n<\/td>\n<\/tr>\n
14:30-15:00<\/td>\nLing Zhou\n\uff08Southwestern University of Finance and Economics\uff09\n<\/td>\nConfederated learning and Inference<\/td>\n<\/tr>\n
15:00-15:30<\/td>\nZheng Zhang\n\uff08Renmin University of China\uff09\n<\/td>\nNonparametric Estimation of Continuous Treatment Effect with Measurement Error<\/td>\n<\/tr>\n
15:30-15:50<\/td>\nCoffee Break<\/td>\n<\/tr>\n
15:50-16:20<\/td>\nLin Liu\n\uff08Shanghai Jiaotong University\uff09\n<\/td>\nA novel stable higher-order influence function estimators for doubly-robust functionals<\/td>\nWei Chen\n(Microsoft)\n<\/td>\n<\/tr>\n
16:20-16:50<\/td>\nWei Li\n\uff08Renmin University of China\uff09\n<\/td>\nEstimation and inference for high-dimensional nonparametric additive instrumental-variables regression<\/td>\n<\/tr>\n
16:50-17:20<\/td>\nChang Liu\n\uff08Microsoft\uff09\n<\/td>\nImproving out-of-Distribution Performance of Machine Learning Models from a Causal Perspective<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n","protected":false},"excerpt":{"rendered":"

Causal inference is one of the hotspots in data science and artificial intelligence research in recent years, and has received extensive attention from academia and industry. This workshop aims to further promote academic exchanges between researchers in the field of causal inference and machine learning, and explore the combination of causal inference and machine learning. […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2022-04-02","msr_enddate":"2022-04-02","msr_location":"Beijing, China","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[],"msr-event-type":[243921,210063],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-831499","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-event-type-academic-event","msr-event-type-workshop","msr-locale-en_us"],"msr_about":"\n\n\n\n\n

Causal inference is one of the hotspots in data science and artificial intelligence research in recent years, and has received extensive attention from academia and industry. This workshop aims to further promote academic exchanges between researchers in the field of causal inference and machine learning, and explore the combination of causal inference and machine learning. This workshop is fortunate to invite 12 experts in related fields to give academic reports and conduct extensive academic discussions in related fields. The workshop will be held on April 2, 2022 in the lecture hall on the fourth floor of the Institute of Computing Technology, Chinese Academy of Sciences. <\/p>\n\n\n\n

<\/p>\n\n\n\n

Organizing Committee<\/h3>\n\n\n\n

Chairs:
<\/strong>Zhi-Ming Ma, Xueqi Cheng, Tie-Yan Liu<\/p>\n\n\n\n

Committee members:
<\/strong>Jiafeng Guo, Wei Chen, Changliang Zou, Chuan Zhou, Qi Meng, Ruqing Zhang, Lijun Sun<\/p>\n\n\n\n\n\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Date<\/th>\nTime<\/th>\nReporter<\/th>\nTitle<\/th>\nChair<\/th>\n<\/tr>\n<\/thead>\n
April 1<\/td>\n14:00-22:00<\/td>\nRegistration<\/td>\n<\/tr>\n
April 2<\/td>\n08:50-09:10<\/td>\nOpening ceremony<\/td>\nJiafeng Guo (CAS)<\/td>\n<\/tr>\n
09:10-09:40<\/td>\nHuazhen Lin\n\uff08Southwestern University of Finance and Economics\uff09\n<\/td>\nRobust and efficient estimation for treatment effect in causal inference<\/td>\nWei Chen\n(CAS)\n<\/td>\n<\/tr>\n
09:40-10:10<\/td>\nPeng Cui\n\uff08Tsinghua University\uff09\n<\/td>\nCausal-Inspired Stable Learning<\/td>\n<\/tr>\n
10:10-10:40<\/td>\nWei Lin\n\uff08Peking University\uff09\n<\/td>\nDeconfounding with the Blessing of Dimensionality<\/td>\n<\/tr>\n
10:40-11:00<\/td>\nCoffee Break<\/td>\n<\/tr>\n
11:00-11:30<\/td>\nWang Miao\n\uff08Peking University\uff09\n<\/td>\nCausal Inference, Observational Research and the Nobel Prize in Economics<\/td>\nChangliang Zou\n(NKU)\n<\/td>\n<\/tr>\n
11:30-12:00<\/td>\nRui Ding\n\uff08Microsoft\uff09\n<\/td>\nSupervised Causal Learning: A New Frontier of Causal Discovery<\/td>\n<\/tr>\n
12:00-12:30<\/td>\nLei Wang\n\uff08Nankai University\uff09\n<\/td>\nGeneralized regression estimators for average treatment effect with multicollinearity in high-dimensional covariates<\/td>\n<\/tr>\n
12:30-14:00<\/td>\nLunch<\/td>\n<\/tr>\n
14:00-14:30<\/td>\nWei Chen\n\uff08Microsoft\uff09\n<\/td>\nCombinatorial Causal Bandit<\/td>\nWang Miao\n(PKU)\n<\/td>\n<\/tr>\n
14:30-15:00<\/td>\nLing Zhou\n\uff08Southwestern University of Finance and Economics\uff09\n<\/td>\nConfederated learning and Inference<\/td>\n<\/tr>\n
15:00-15:30<\/td>\nZheng Zhang\n\uff08Renmin University of China\uff09\n<\/td>\nNonparametric Estimation of Continuous Treatment Effect with Measurement Error<\/td>\n<\/tr>\n
15:30-15:50<\/td>\nCoffee Break<\/td>\n<\/tr>\n
15:50-16:20<\/td>\nLin Liu\n\uff08Shanghai Jiaotong University\uff09\n<\/td>\nA novel stable higher-order influence function estimators for doubly-robust functionals<\/td>\nWei Chen\n(Microsoft)\n<\/td>\n<\/tr>\n
16:20-16:50<\/td>\nWei Li\n\uff08Renmin University of China\uff09\n<\/td>\nEstimation and inference for high-dimensional nonparametric additive instrumental-variables regression<\/td>\n<\/tr>\n
16:50-17:20<\/td>\nChang Liu\n\uff08Microsoft\uff09\n<\/td>\nImproving out-of-Distribution Performance of Machine Learning Models from a Causal Perspective<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n","tab-content":[],"msr_startdate":"2022-04-02","msr_enddate":"2022-04-02","msr_event_time":"","msr_location":"Beijing, China","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"April 2, 2022","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"Causal inference is one of the hotspots in data science and artificial intelligence research in recent years, and has received extensive attention from academia and industry. This workshop aims to further promote academic exchanges between researchers in the field of causal inference and machine learning, and explore the combination of causal inference and machine learning. This workshop is fortunate to invite 12 experts in related fields to give academic reports and conduct extensive academic discussions…","msr_research_lab":[199560],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[802999],"related-projects":[],"related-opportunities":[],"related-publications":[],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/831499","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":18,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/831499\/revisions"}],"predecessor-version":[{"id":857784,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/831499\/revisions\/857784"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=831499"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=831499"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=831499"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=831499"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=831499"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=831499"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=831499"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=831499"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=831499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}