Abstract
We consider methods for estimating causal effects of treatments when treatment assignment is unconfounded with outcomes conditional on a possibly large set of covariates. Robins and Rotnitzky (1995) suggested combining regression adjustment with weighting based on the propensity score (Rosenbaum and Rubin, 1983). We adopt this approach, allowing for a flexible specification of both the propensity score and the regression function. We apply these methods to data on the effects of right heart catheterization (RHC) studied in Connors et al (1996), and we find that our estimator gives stable estimates over a wide range of values for the two parameters governing the selection of variables.
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Hirano, K., Imbens, G.W. Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization. Health Services & Outcomes Research Methodology 2, 259–278 (2001). https://doi.org/10.1023/A:1020371312283
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DOI: https://doi.org/10.1023/A:1020371312283