A formal causal interpretation of the case‐crossover design

Zach Shahn, Miguel A. Hernán and James M. Robins

Biometrics, 2023, vol. 79, issue 2, 1330-1343

Abstract: The case‐crossover design of Maclure is widely used in epidemiology and other fields to study causal effects of transient treatments on acute outcomes. However, its validity and causal interpretation have only been justified under informal conditions. Here, we place the design in a formal counterfactual framework for the first time. Doing so helps to clarify its assumptions and interpretation. In particular, when the treatment effect is nonnull, we identify a previously unnoticed bias arising from strong common causes of the outcome at different person‐times. We analyze this bias and demonstrate its potential importance with simulations. We also use our derivation of the limit of the case‐crossover estimator to analyze its sensitivity to treatment effect heterogeneity, a violation of one of the informal criteria for validity. The upshot of this work for practitioners is that, while the case‐crossover design can be useful for testing the causal null hypothesis in the presence of baseline confounders, extra caution is warranted when using the case‐crossover design for point estimation of causal effects.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:79:y:2023:i:2:p:1330-1343

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