with Philip Stark and Jasjeet Sekhon
The goal of observational studies is to make inferences the causal effect of a treatment on some outcome of interest. The “fundamental problem of causal inference” makes this difficult: we are only able to see each individual’s outcome after treatment or no treatment, but not both. To estimate the effect of treatment, one must use a control group as the counterfactual. However, the treatment and control groups may be unalike in ways other than the treatment. Ideally, to adjust for these confounding variables, one would estimate the difference in outcomes between cases and controls who are identical with respect to all confounders, then average over the pairs. In practice, the large number of covariates accounted for makes this impossible. We propose to circumvent the problem of balancing treatment and control groups with a novel method for matching and estimation. This two-step procedure combines predictive modeling with nonparametric hypothesis testing to assess whether or not the treatment assignment provides any additional information about the outcome beyond what we would expect given all other covariates.