|
|
The
propensity score plays a central role in a variety of causal
inference settings. In particular, matching and weighting methods
based on the estimated propensity score have become increasingly
common in observational studies. Despite their popularity and
theoretical appeal, the main practical difficulty of these methods
is that the propensity score must be estimated. Researchers have
found that slight misspecification of the propensity score model can
result in substantial bias of estimated treatment effects. In this
paper, we introduce covariate balancing propensity score (CBPS)
methodology, which models treatment assignment while optimizing the
covariate balance. This is done by exploiting the dual
characteristics of the propensity score as a covariate balancing
score and the conditional probability of treatment assignment. The
estimation of the CBPS is done within the generalized method of
moments or empirical likelihood framework. We find that the CBPS
dramatically improves the poor empirical performance of propensity
score matching and weighting methods reported in the literature. We
also show that the CBPS can be extended to a number of other
important settings, including the estimation of the generalized
propensity score for non-binary treatments and the generalization of
experimental estimates to a target population. Open-source software is
available for implementing the proposed methods. |
``Optimal Covariate Balancing Conditions in
Propensity Score Estimation ,'' develops more theory
and improvements. |
``Covariate Balancing
Propensity Score for General Treatment Regimes,''
generalizes the CBPS to the multi-valued and continuous treatments.
|
``Robust
Estimation of Inverse Probability Weights for Marginal Structural
Models,'' generalizes the CBPS to the longitudinal data
settings. |
``High
Dimensional Propensity Score Estimation via Covariate
Balancing,'' extends the CBPS to high-dimensional
settings. |
Fong, Christian, Marc Ratkovic, and Kosuke
Imai. ``CBPS: R Package
for Covariate Balancing Propensity Score.''
available through The Comprehensive R
Archive Network. 2014. |
For an independent examination of CBPS's
performance, see Richard Wyss et al. (2014). ``The
Role of Prediction Modeling in Propensity Score Estimation: An
Evaluation of Logistic Regression, bCART, and the
Covariate-Balancing Propensity Score'' American
Journal of Epidemiology, and Markus Frolich et
al. (2015). ``The Finite Sample Performance of
Semi- and Nonparametric Estimators for Treatment Effects and
Policy Evaluation'' IZA Discussion Paper
No. 8756. |
See this World
Bank blog post that discusses this paper.
|