Universal Difference-in-Differences for Causal Inference in Epidemiology

Details

• Personal Author:
  Park C ; Richardson DB ; Tchetgen Tchetgen EJ
• Description:
  Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and postexposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, for example, if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits nonadditive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called universal difference-in-differences. We describe and illustrate both fully parametric and more robust semiparametric universal difference-in-differences estimators in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil. [Description provided by NIOSH]
• Subjects:
  Epidemiology Occupational Health Safety
• Keywords:
  Author Keywords: Difference-in-differences Equi-confounding Extended Propensity Score Generalized Linear Models Odds Ratios Selection Bias Statistical Analysis Statistical Quality Control
• ISSN:
  1044-3983
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; North Carolina ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 9 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  16-22
• Volume:
  35
• Issue:
  1
• NIOSHTIC Number:
  nn:20069503
• Citation:
  Epidemiology 2024 Jan; 35(1):16-22
• Contact Point Address:
  Chan Park, Department of Statistics and Data Science, University of Pennsylvania, 265 S 37th St, Philadelphia, PA 19104
• Email:
  chanpk@wharton.upenn.edu
• Federal Fiscal Year:
  2024
• Performing Organization:
  University of North Carolina-Chapel Hill
• Peer Reviewed:
  True
• Start Date:
  20190801
• Source Full Name:
  Epidemiology
• End Date:
  20220731
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:20a85ee800f1f9a037f6536347dbf459c41e0cf8b6f003598117a8b3835dc7dcf2b9cf7b6648525a734db759bb61c9019cbb7ca05cfc0744f62aa4e04d551a6d
• Download URL:
  https://stacks.cdc.gov/view/cdc/207936/cdc_207936_DS1.pdf
• File Type:
  [PDF - 737.43 KB ]