2019
DOI: 10.1214/18-aos1698 View full text |Buy / Rent full text
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Abstract: In observational studies, propensity scores are commonly estimated by maximum likelihood but may fail to balance high-dimensional pre-treatment covariates even after specification search. We introduce a general framework that unifies and generalizes several recent proposals to improve covariate balance when designing an observational study. Instead of the likelihood function, we propose to optimize special loss functions-covariate balancing scoring rules (CBSR)-to estimate the propensity score. A CBSR is uniqu… Show more

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