stratamatch: Prognostic ScoreStratification using a Pilot Design

TL;DR

Stratamatch introduces a stratified matching approach using a pilot design to improve computational efficiency and statistical power in large observational studies for causal inference.

Contribution

It provides a new implementation supporting stratified matching with a pilot design, enhancing scalability and precision in propensity score matching.

Findings

Enables efficient matching on large datasets.

Increases precision of effect estimates.

Improves power in sensitivity analyses.

Abstract

Optimal propensity score matching has emerged as one of the most ubiquitous approaches for causal inference studies on observational data; However, outstanding critiques of the statistical properties of propensity score matching have cast doubt on the statistical efficiency of this technique, and the poor scalability of optimal matching to large data sets makes this approach inconvenient if not infeasible for sample sizes that are increasingly commonplace in modern observational data. The stratamatch package provides implementation support and diagnostics for `stratified matching designs,' an approach which addresses both of these issues with optimal propensity score matching for large-sample observational studies. First, stratifying the data enables more computationally efficient matching of large data sets. Second, stratamatch implements a `pilot design' approach in order to stratify by a prognostic score, which may increase the precision of the effect estimate and increase power in sensitivity analyses of unmeasured confounding.