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Activity Number: 135 - Move Non/Semiparametrics Forward in Causal Inference, Missing Data Analysis, and Data Integration
Type: Invited
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Nonparametric Statistics
Abstract #308030
Title: Combining Multiple Observational Data Sources to Estimate Causal Effects
Author(s): Peng Ding* and Shu Yang
Companies: University of California, Berkeley and North Carolina State University
Keywords: Calibration; Causal inference; Inverse probability weighting; Missing confounder; Two-phase sampling

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider the 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. Our 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. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators.

Authors who are presenting talks have a * after their name.

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