BIOSTATISTICS SEMINAR SERIES
Xiaochun Li, Ph.D.
Associate Professor, Department of Biostatistics, School of Medicine, Fairbanks School of Public Health, Indiana University
Title: A Machine Learning Approach to Causal Inference in the Presence of Missing Data
Abstract: Observational medical databases increasingly find uses for comparative effectiveness and safety research. However, the lack of analytic methods that simultaneously handle the issues of missing data and confounding bias along with the onus of model specification, limit the use of these valuable data sources. We derived a novel machine-learning approach based on trees to estimate the average treatment effect. In order to evaluate causal estimation by model-free machine-learning methods in data with incomplete observations, we conducted a simulation study with data generated from known models of exposure, outcome and missing mechanisms. Thus the true causal effect was known and used as the benchmark for evaluations. Two settings were studied. We compared the bias and standard error of causal estimates from our method to a multiply robust parametric method, the complete case analysis (CC) and a regression analysis after multiple imputations (MI). The proposed methods were applied to a real observational data set of implantable cardioverter defibrillator use.
Bio3 Seminar Series sponsored by
Department of Biostatistics, Bioinformatics & Biomathematics (DBBB)
Friday, September 13 at 10:00am to 11:00am
Medical and Dental Building, Proctor Harvey, C104
3900 Reservoir Road, N.W., Washington
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