Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization
 Keisuke Hirano,
 Guido W. Imbens
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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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 Title
 Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization
 Journal

Health Services and Outcomes Research Methodology
Volume 2, Issue 34 , pp 259278
 Cover Date
 20011201
 DOI
 10.1023/A:1020371312283
 Print ISSN
 13873741
 Online ISSN
 15729400
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 casual inference
 propensity score
 treatment effects
 right heart catheterization
 variable selection
 Industry Sectors
 Authors

 Keisuke Hirano ^{(1)}
 Guido W. Imbens ^{(2)}
 Author Affiliations

 1. Department of Economics, University of Miami, PO Box 248126, Coral Gables, FL, 331246550
 2. Department of Economics, University of California, 549 Evans Hall, #3880, Berkeley, CA, 947203880