Estimating causal effects with optimization-based methods: A review and empirical comparison.

ABSTRACT: In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given. To achieve this balance, there exist a wide variety of methods. In particular, several methods based on optimization models have been recently proposed in the causal inference literature. While these optimization-based methods empirically showed an improvement over a limited number of other causal inference methods in their relative ability to balance the distributions of covariates and to estimate causal effects, they have not been thoroughly compared to

SUBMITTER: Cousineau M 

PROVIDER: S-EPMC12657020 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature