Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization
@article{Hirano2001EstimationOC, title={Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization}, author={Keisuke Hirano and Guido Imbens}, journal={Health Services and Outcomes Research Methodology}, year={2001}, volume={2}, pages={259-278} }
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…
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References
SHOWING 1-10 OF 23 REFERENCES
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Economics, Mathematics
- 2000
It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions.
The central role of the propensity score in observational studies for causal effects
- Economics
- 1983
Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.…
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
- Mathematics, Economics
- 1984
Abstract The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that…
On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
- Mathematics, Economics
- 1998
The role of propensity score in the efficient estimation of the average treatment effects is examined. If the treatment is ignorable given some observed characteristics, it is shown that the…
Marginal Structural Models versus Structural nested Models as Tools for Causal inference
- Mathematics, Economics
- 2000
This paper describes an alternative new class of causal models — the (non-nested) marginal structural models (MSMs) and describes a class of semiparametric estimators for the parameters of these new models under a sequential randomization assumption.
Characterizing the effect of matching using linear propensity score methods with normal distributions
- Economics
- 1992
SUMMARY Matched sampling is a standard technique for controlling bias in observational studies due to specific covariates. Since Rosenbaum & Rubin (1983), multivariate matching methods based on…
Analysis of semiparametric regression models for repeated outcomes in the presence of missing data
- Mathematics
- 1995
Abstract We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a…
Semiparametric regression estimation in the presence of dependent censoring
- Mathematics
- 1995
SUMMARY We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured…
Matching As An Econometric Evaluation Estimator
- Economics
- 1998
This paper develops the method of matching as an econometric evaluation estimator. A rigorous distribution theory for kernel-based matching is presented. The method of matching is extended to more…
Matching using estimated propensity scores: relating theory to practice.
- MathematicsBiometrics
- 1996
These results delineate the wide range of settings in which matching on estimated linear propensity scores performs well, thereby providing useful information for the design of matching studies and applying theoretical approximations to practice.