The Incremental Propensity Score Approach for Diversity Science

Abstract

Addressing core questions in diversity science requires quantifying causal effects (e.g., what drives social inequities and how to reduce them). Conventional approaches target the average causal effect (ACE), but ACE-based analyses suffer from limitations that undermine their relevance for diversity science. In this article, we introduce a novel alternative from the causal inference literature: the so-called incremental propensity score (IPS). First, we explain why the IPS is well suited for investigating core queries in diversity science. Unlike the ACE, the IPS does not demand conceptualizing unrealistic counterfactual scenarios in which everyone in the population is uniformly exposed versus unexposed to a causal factor. Instead, the IPS focuses on the effect of hypothetically shifting individuals’ chances of being exposed along a continuum. This allows seeing how the effect may be graded, offering a more realistic and policy-relevant quantification of the causal effect than a single ACE estimate. Moreover, the IPS does not require the positivity assumption, a necessary condition for estimating the ACE but which rarely holds in practice. Next, to broaden accessibility, we provide a step-by-step guide on estimating the IPS using R, a free and popular software. Finally, we illustrate the IPS using two real-world examples. The current article contributes to the methodological advancement in diversity science and offers researchers a more realistic, relevant, and meaningful approach.