Title: Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications,
Abstract: When investigators seek to estimate causal effects, they often assume that
selection into treatment is based only on observed covariates. Under this
identification strategy, analysts must adjust for observed confounders. While
basic regression models have long been the dominant method of statistical
adjustment, more robust methods based on matching or weighting have become more
common. Of late, even more flexible methods based on machine learning methods
have been developed for statistical adjustment. These machine learning methods
are designed to be black box methods with little input from the researcher.
Recent research used a data competition to evaluate various methods of
statistical adjustment and found that black box methods out performed all other
methods of statistical adjustment. Matching methods with covariate
prioritization are designed for direct input from substantive investigators in
direct contrast to black methods. In this article, we use a different research
design to compare matching with covariate prioritization to black box methods.
We use black box methods to replicate results from five studies where matching
with covariate prioritization was used to customize the statistical adjustment
in direct response to substantive expertise. We find little difference across
the methods. We conclude with advice for investigators.