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20181212 Peng Ding:Combining multiple observational data sources to estimate causal effects
时间:2018-12-04

报告时间:20181212 10:00-11:00

报告地点:明主1016会议室

报告主题:Combining multiple observational data sources to estimate causal effects

报告摘要:The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. As an important example in causal inference, we consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. The proposed framework applies to asymptotically normal estimators, including the commonly-used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. Coupled with appropriate bootstrap procedures, our method is straightforward to implement using software routines for existing estimators.

报告人简介:Peng Ding received Ph.D. from the Harvard Statistics Department in May 2015 and worked as a postdoctoral researcher in the Harvard Epidemiology Department until December 2015. Since January 2016, he has been Assistant Professor in the Statistics Department of University of California, Berkeley. His research interests include causal inference, missing data, and experimental design.

推荐阅读文章:https://arxiv.org/abs/1801.00802