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When investigators seek to estimate causal effects, they often assume thatselection into treatment is based only on observed covariates. Under thisidentification strategy, analysts must adjust for observed confounders. Whilebasic regression models have long been the dominant method of statisticaladjustment, more robust methods based on matching or weighting have become morecommon. Of late, even more flexible methods based on machine learning methodshave been developed for statistical adjustment. These machine learning methodsare designed to be black box methods with little input from the researcher.Recent research used a data competition to evaluate various methods ofstatistical adjustment and found that black box methods out performed all othermethods of statistical adjustment. Matching methods with covariateprioritization are designed for direct input from substantive investigators indirect contrast to black methods. In this article, we use a different researchdesign to compare matching with covariate prioritization to black box methods.We use black box methods to replicate results from five studies where matchingwith covariate prioritization was used to customize the statistical adjustmentin direct response to substantive expertise. We find little difference acrossthe methods. We conclude with advice for investigators.

Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications