Estimating Potential Outcome Distributions with Collaborating Causal Networks.

ABSTRACT: Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE al

SUBMITTER: Zhou T 

PROVIDER: S-EPMC10769464 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature