Machine Learning for Causal Inference: On the Use of Cross-fit Estimators.

ABSTRACT:

Background

Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for inference. Doubly robust cross-fit estimators have been proposed to yield better statistical properties.

Methods

We conducted a simulation study to assess the performance of several different estimators for the average causal effect. The data generating mechanisms for the simulated treatment and outcome included log-transforms, polynomial terms, and discontinuities. We compared singly robust estimators (g-computation, inverse probability weighting) and doubly robust estimators (augmented inverse probability weighting, targeted maximum likelihood estimation). We estimated nuisance functions

SUBMITTER: Zivich PN 

PROVIDER: S-EPMC8012235 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature