Designing Difference in Difference Studies: Best Practices for Public Health Policy Research.

By Coady Wing, K. Simon, Ricardo A. Bello‐Gomez. Annual Review of Public Health, 2018

1,302 citations recorded by the source corpus.

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.

Subfields: Economics and Econometrics, General Health Professions, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/230914bb6a83c28c3e4e20677383d88c61f09538

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Citations in the corpus

Use of Quasi-Experimental Research Designs in Education Research: Growth, Promise, and Challenges (2020, background): “For example, the parallel trends assumption inherent in DID designs are central to the validity of the estimated effects (see Wing et al., 2018, for a review of the robustness checks necessary to rigorously defend these assumptions).”
Ethical Machine Learning in Healthcare (2020, background): “Administrative Health Records In addition to RCTs and EHR, health care billing claims data, clinical registries, and linked health survey data are also common data sources in population health and health policy research (67, 68), with known biases in which populations are followed, and who is able t”
Effects of social distancing policy on labor market outcomes (2020, neutral): “This version of the DiD model relies on the common trends and strict exogeneity assumptions (Wing et al., 2018).”