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计量、金融与大数据分析Workshop：Nested Instrumental Variables Design: Switcher Average Treatment Effect, Identification, Efficient Estimation and Generalizability
发布日期：2024-04-26 12:00 来源：
[4月26日] 计量金融与大数据分析Workshop
题目：Nested Instrumental Variables Design: Switcher Average Treatment Effect, Identification, Efficient Estimation and Generalizability
摘要：Instrumental variables (IV) are a commonly used tool to estimate causal effects from non-randomized data. A prototype of an IV is a randomized trial with non-compliance where the randomized treatment assignment serves as an IV for the non-ignorable treatment received. Under a monotonicity assumption, a valid IV non-parametrically identifies the average treatment effect among a non-identifiable complier subgroup, whose generalizability is often under debate. In many studies, there could exist multiple versions of an IV, for instance, different nudges to take the same treatment in different study sites in a multi-center clinical trial. These different versions of an IV may result in different compliance rates and offer a unique opportunity to study IV estimates’ generalizability. In this article, we introduce a novel nested IV assumption and study identification of the average treatment effect among two latent subgroups: always-compliers and switchers, who are defined based on the joint potential treatment received under two versions of a binary IV. We derive the efficient influence function for the SWitcher Average Treatment Effect (SWATE) and propose efficient estimators. We then propose formal statistical tests of the generalizability of IV estimates based on comparing the conditional average treatment effect among the always-compliers and that among the switchers under the nested IV framework. We apply the proposed framework and method to the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and study the causal effect of colorectal cancer screening and its generalizability.
主讲人：Rui Wang, University of Washington
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2024年4月26日（周五）10:00 AM -- 11:30 AM
地点：线上会议
Zoom 会议链接：
https://us06web.zoom.us/j/89615130445?pwd=dotWSa3a4KS9HOHzMn0SprtSZhXMif.1
会议号: 896 1513 0445
密码: 295620
主讲人简介：
Dr Rui Wang is major in Biostatistics. Currently, he is working on several instrumental variables related projects. These include assessing the generalizability of instrumental variable estimates, employing instrumental variables to identify and estimate causal effects in clinical trials under complex sampling designs, and addressing challenges associated with weak instruments. He has published papers in Atherosclerosis、Vaccines and Statistics in Medicine.
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计量、金融与大数据分析Workshop：Nested Instrumental Variables Design: Switcher Average Treatment Effect, Identification, Efficient Estimation and Generalizability
发布日期：2024-04-26 12:00 来源：
[4月26日] 计量金融与大数据分析Workshop
题目：Nested Instrumental Variables Design: Switcher Average Treatment Effect, Identification, Efficient Estimation and Generalizability
摘要：Instrumental variables (IV) are a commonly used tool to estimate causal effects from non-randomized data. A prototype of an IV is a randomized trial with non-compliance where the randomized treatment assignment serves as an IV for the non-ignorable treatment received. Under a monotonicity assumption, a valid IV non-parametrically identifies the average treatment effect among a non-identifiable complier subgroup, whose generalizability is often under debate. In many studies, there could exist multiple versions of an IV, for instance, different nudges to take the same treatment in different study sites in a multi-center clinical trial. These different versions of an IV may result in different compliance rates and offer a unique opportunity to study IV estimates’ generalizability. In this article, we introduce a novel nested IV assumption and study identification of the average treatment effect among two latent subgroups: always-compliers and switchers, who are defined based on the joint potential treatment received under two versions of a binary IV. We derive the efficient influence function for the SWitcher Average Treatment Effect (SWATE) and propose efficient estimators. We then propose formal statistical tests of the generalizability of IV estimates based on comparing the conditional average treatment effect among the always-compliers and that among the switchers under the nested IV framework. We apply the proposed framework and method to the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and study the causal effect of colorectal cancer screening and its generalizability.
主讲人：Rui Wang, University of Washington
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2024年4月26日（周五）10:00 AM -- 11:30 AM
地点：线上会议
Zoom 会议链接：
https://us06web.zoom.us/j/89615130445?pwd=dotWSa3a4KS9HOHzMn0SprtSZhXMif.1
会议号: 896 1513 0445
密码: 295620
主讲人简介：
Dr Rui Wang is major in Biostatistics. Currently, he is working on several instrumental variables related projects. These include assessing the generalizability of instrumental variable estimates, employing instrumental variables to identify and estimate causal effects in clinical trials under complex sampling designs, and addressing challenges associated with weak instruments. He has published papers in Atherosclerosis、Vaccines and Statistics in Medicine.
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