JSM 2011 Online Program

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Abstract Details

Activity Number: 529
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #302174
Title: Evaluation of Alternative Propensity Score-Based Causal Inference Approaches
Author(s): Bing Yu*+ and Guanglei Hong
Companies: University of Toronto and The University of Chicago
Address: 30 Charles St. West, 1407, Toronto, ON, M4Y 1R5, Canada
Keywords: bias ; causal inference ; confidence interval ; MSE ; prognostic score ; variance
Abstract:

Extant research on variable selection for propensity score (PS) models selects covariates to be included in a PS model based on their relationships with treatment and/or outcome. In general, including outcome predictors in a PS model improves the precision of treatment effect estimation without increasing bias. This study extends this line of research by incorporating three further aspects. Using simulation data, first we will evaluate the relative effectiveness of combining propensity score with prognostic score adjustment or with strong outcome predictor adjustment. Secondly, we will consider a Normal distributed outcome versus a Bernoulli distributed outcome. Finally, we will test the robustness of alternative approaches under a range of model misspecifications, including omitted covariates, omitted nonlinear terms, or omitted interaction terms in a PS model, a prognostic score model, or an outcome model. Evaluation criteria include bias, precision, MSE, remaining sample size after stratification, and confidence interval coverage percentage.


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