This talk is based on several projects with multiple people, including, James Robins, Eric Tchetgen Tchetgen, Whitney Newey, Subhabrata Sen, Lingling Li, Lin Liu, and Aad Van der Vaart.
Research activities in observational studies have seen a recent surge owing to multiple factors. Among others, these factors include our ability and need to collect large amounts of data along with the development of powerful machine learning algorithms. Consequently, researchers have focused on tailoring this powerful machinery to answer questions in studies with both a large number of observations and potentially many covariates. Typical examples of such problems include, but are not limited to, estimating causal effects of administered treatments or understanding relevant parameters in missing data studies. However, this large array of research activity often comes with the burden of assumptions — the heart of which lie in justifying low bias for suggested statistical procedures. Driven by this need to reduce bias, in this talk, I will discuss a unified framework to obtain “optimal” inferential procedures for quantities of interest in common observational studies. This framework will be based on extending classical semiparametric theory to understand necessary and sufficient conditions under which efficient inference is possible. Such an understanding will be crucial to de-mystify the assumptions strewn across the literature of observational studies. Moreover, I will also demonstrate the power of this suggested methodology in producing valid inference (i.e. confidence intervals with correct coverage) under provably minimal conditions—where most other procedures in literature might fail. Finally, proceeding through theoretical and numerical analyses, I will also try to discuss broader research questions attached to this paradigm and elaborate on its relevance in scientific research. Light refreshments for seminar guests will be served at 3:10 p.m. in 3755
Department of BiostatisticsOn Inference in Observational Studies
Rajarshi Mukherjee, PhD, Assistant Professor - Department of Biostatistics - Harvard T. H. Chan School of Public Health
April 4, 2019
3:30 pm - 5:00 pm
3755 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029
Sponsored by: Department of Biostatistics
Contact Information: Zhenke Wu (zhenkewu@umich.edu) & Peisong Han (peisong)
This talk is based on several projects with multiple people, including, James Robins, Eric Tchetgen Tchetgen, Whitney Newey, Subhabrata Sen, Lingling Li, Lin Liu, and Aad Van der Vaart.
Research activities in observational studies have seen a recent surge owing to multiple factors. Among others, these factors include our ability and need to collect large amounts of data along with the development of powerful machine learning algorithms. Consequently, researchers have focused on tailoring this powerful machinery to answer questions in studies with both a large number of observations and potentially many covariates. Typical examples of such problems include, but are not limited to, estimating causal effects of administered treatments or understanding relevant parameters in missing data studies. However, this large array of research activity often comes with the burden of assumptions — the heart of which lie in justifying low bias for suggested statistical procedures. Driven by this need to reduce bias, in this talk, I will discuss a unified framework to obtain “optimal” inferential procedures for quantities of interest in common observational studies. This framework will be based on extending classical semiparametric theory to understand necessary and sufficient conditions under which efficient inference is possible. Such an understanding will be crucial to de-mystify the assumptions strewn across the literature of observational studies. Moreover, I will also demonstrate the power of this suggested methodology in producing valid inference (i.e. confidence intervals with correct coverage) under provably minimal conditions—where most other procedures in literature might fail. Finally, proceeding through theoretical and numerical analyses, I will also try to discuss broader research questions attached to this paradigm and elaborate on its relevance in scientific research. Light refreshments for seminar guests will be served at 3:10 p.m. in 3755