Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties.

ABSTRACT: We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator, which utilizes posterior distributions of model parameters and (1) results in good frequentist properties in small samples, (2) is based on a single run of a Markov chain Monte Carlo (MCMC) algorithm, and (3) improves over frequentist measures of uncertainty which rely on asymptotic properties. We consider a flexible framework for modeling the treatment and outcome processes within the Bayesian paradigm that reduces model dependence, accommodates nonlinearity, and achieves dimen

SUBMITTER: Antonelli J 

PROVIDER: S-EPMC8209114 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature