Causal Inference in Nonprofit Management and Public Administration

Andrew Heiss and Meng Ye, “Causal Inference in Nonprofit Management and Public Administration”


The “causal revolution” in the broader social sciences has led to a new field of observational causal inference. However, these advancements have been less used among nonprofit scholars and practitioners. In this article, we provide a methodological guide for applying causal inference tools within nonprofit studies. We walk through practical examples of model-based and design-based approaches from epidemiology and econometrics to observational causal inference, including causal models, inverse probability weighting, difference-in-differences, regression discontinuity, and instrumental variables. We include empirical illustrations of these approaches by replicating existing nonprofit research and program evaluations. We conclude with a call for more careful and explicit attempts at making causal claims in nonprofit studies.