Abstract
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.
Highlights
Causal inference is a key challenge in public health policy research intended to assess past policies and help decide future priorities
We focus on the design of quasi-experimental studies that compare the outcomes of groups exposed to different policies and environmental factors at different times
Quasi-experimental research designs can be an effective way to learn about causal relationships that are important for public health science and public health policy
Summary
Causal inference is a key challenge in public health policy research intended to assess past policies and help decide future priorities. In the two-group two-period DID design, the common trend assumption amounts to a simple statistical model of the treated and untreated potential outcomes.
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