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

Annu Rev Public Health. 2018 Apr 1;39:453-469. doi: 10.1146/annurev-publhealth-040617-013507. Epub 2018 Jan 12.


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.

Keywords: causal inference; difference in difference; policy analysis; quasi-experiments; research design.

MeSH terms

  • Causality
  • Data Interpretation, Statistical
  • Health Policy*
  • Humans
  • Policy Making*
  • Public Health*
  • Public Policy
  • Research Design*