Optimally Balanced Gaussian Process Propensity Scores for Estimating Treatment Effects
| Autor: | Brian G. Vegetabile, Daniel L. Gillen, Hal S. Stern |
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| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Causal Inference Economics and Econometrics Average treatment effect Computer science Statistics & Probability Nonparametric Estimation Article symbols.namesake Covariate balance Statistics Covariate Econometrics Gaussian process Demography Hyperparameter Covariance matrix Propensity score matching Metric (mathematics) symbols Observational study Gaussian Process Statistics Probability and Uncertainty Non-parametric estimation Social Sciences (miscellaneous) |
| Zdroj: | J R Stat Soc Ser A Stat Soc Journal of the Royal Statistical Society. Series A, (Statistics in Society), vol 183, iss 1 |
| ISSN: | 1467-985X 0964-1998 |
| Popis: | Summary Propensity scores are commonly employed in observational study settings where the goal is to estimate average treatment effects. The paper introduces a flexible propensity score modelling approach, where the probability of treatment is modelled through a Gaussian process framework. To evaluate the effectiveness of the estimated propensity score, a metric of covariate imbalance is developed that quantifies the discrepancy between the distributions of covariates in the treated and control groups. It is demonstrated that this metric is ultimately a function of the hyperparameters of the covariance matrix of the Gaussian process and therefore it is possible to select the hyperparameters to optimize the metric and to minimize overall covariate imbalance. The effectiveness of the Gaussian process method is compared in a simulation against other methods of estimating the propensity score and the method is applied to data from a study of Dehejia and Wahba in 1999 to demonstrate benchmark performance within a relevant policy application. |
| Databáze: | OpenAIRE |
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