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The estimation of causal effects is a central goal of social science research. In this project, we develop propensity score methods that can help empirical researchers conduct reliable and efficient causal inference in both experimental and observational studies. First, we generalize the propensity score methods, which are originally developed for binary treatments, to arbitrary treatment types including continuous and multinomial treatments. This is an important generalization because many causal variables of interest in social science research are not binary. We show how our generalization preserves the basic advantages of the propensity score methods; dimension reduction, balance diagnostics, use of simple nonparametric estimation methods such as subclassification. Second, develop the robust estimation method of propensity scores in a variety of situations. The key idea is to estimate propensity score such that the resulting covariate balance is optimized. Our proposed method, Covariate Balancing Propensity Score (CBPS), is simple and yet significantly outperforms the standard estimatiod method. The simplicity of CBPS also allows us to extend the method to more complicated situations, including the marginal structural models in panel data settings. |
Generalization of propensity
score: |
Imai, Kosuke, and David A. van
Dyk. (2004). ``Causal
Inference With General Treatment Regimes: Generalizing the
Propensity Score.'' Journal of the American
Statistical Association, Vol. 99, No. 467 (September),
pp. 854-866. |
Zhao, Shandong, David A. van Dyk, and
Kosuke Imai. (2020). ``Propensity-Score Based Methods
for Causal Inference in Observational Studies with Non-Binary
Treatments.'' Statistical Methods in Medical
Research, Vol. 29, No. 3 (March), pp. 709-727.
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Covariate Balancing Propensity
Score (CBPS): |
Imai, Kosuke and Marc
Ratkovic. (2014). ``Covariate Balancing Propensity
Score.'' Journal of the Royal Statistical
Society, Series B (Statistical Methodology), Vol. 76, No. 1
(January), pp. 243-246.
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Imai, Kosuke and Marc
Ratkovic. (2015). ``Robust Estimation of Inverse
Probability Weights for Marginal Structural Models.''
Journal of the American Statistical Association, Vol. 110,
No. 511 (September), pp. 1013-1023. (lead article)
|
Fong, Christian, Chad Hazlett, and Kosuke
Imai. (2018). ``Covariate Balancing Propensity
Score for a Continuous Treatment: Application to the Efficacy of
Political Advertisements.'' Annals of Applied
Statistics, Vol. 12, No. 1, pp. 156-177. |
Fan, Jianqing, Kosuke Imai, Inbeom Lee,
Han Liu, Yang Ning, and Xiaolin Yang. (2023). ``Optimal Covariate Balancing
Conditions in Propensity Score Estimation.''
Journal of Business & Economic Statistics, Vol. 41,
No. 1, pp. 97-110. |
Ning, Yang, Sida Peng, and Kosuke
Imai. (2020). ``Robust
Estimation of Causal Effects via High-Dimensional Covariate
Balancing Propensity Score..'' Biometrika,
Vol. 107, No. 3 (September), pp. 533–554. |
Propensity score for
spatio-temporal point process treatment: |
Papadogeorgou, Georgia, Kosuke Imai, Jason
Lyall, and Fan Li. (2022). ``Causal Inference with
Spatio-temporal Data: Estimating the Effects of Airstrikes on
Insurgent Violence in Iraq.'' Journal of the
Royal Statistical Society, Series B (Statistical
Methodology), Vol. 84, No. 5 (November),
pp. 1969-1999. |
Applications of propensity
score: |
Imai, Kosuke. (2005). ``Do Get-Out-The-Vote Calls
Reduce Turnout? The Importance of Statistical Methods for Field
Experiments.'' American Political Science
Review, Vol. 99, No. 2 (May), pp. 283-300. |
Fong ,Christian, Marc Ratkovic, Chad
Hazlett, and Kosuke
Imai. ``CBPS: R Package
for Covariate Balancing Propensity Score.''
available through The
Comprehensive R Archive
Network. 2016. |
National Science Foundation, (2006-2009).
``Collaborative Research: Generalized Propensity Score Methods,''
(Methodology, Measurement and Statistics Program; SES-0550873). |