Aspects of Causal Inference within the Evenly Matchable Population: The Average Treatment Effect on the Evenly Matchable Units, Visually Guided Cohort Selection, and Bagged One-to-One Matching

Abstract

This dissertation consists of three papers related to causal inference about the evenly matchable units in observational studies of treatment effect. The first paper begins by defining the evenly matchable units in a sample or population in which the effect of a binary treatment is of interest: a unit is evenly matchable if the localized region of the (possibly transformed) covariate space centered on that unit contains at least as many units from the opposite group as from its own group. The paper then defines the average treatment effect on the evenly matchable units (ATM) and continues with a discussion of currently available matching methods that can be used to estimate the ATM, followed by the introduction of three new weighting-based approaches to ATM estimation and a case study illustrating some of these techniques. The second paper introduces a freely available web application that allows analysts to combine information from covariate distributions and estimated propensity scores to create transparent, covariate-based study inclusion criteria as a first step in estimation of the ATM or other quantities. The app, Visual Pruner, is freely available at http://statcomp2.vanderbilt.edu:37212/VisualPruner and is easily incorporated into a reproducible-research workflow. The third paper introduces a new technique for estimation of the ATM or other estimands: bagged one-to-one matching (BOOM), which combines the bias-reducing properties of one-to-one matching with the variance-reducing properties of bootstrap aggregating, or bagging. In this paper I describe the BOOM algorithm in detail and investigate its performance in a simulation study and a case study. In the simulation study, the BOOM estimator achieves as much bias reduction as the estimator based on one-to-one matching, while having much lower variance. In the case study, BOOM yields estimates similar to those from one-to-one matching, with narrower 95% confidence intervals.