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We investigate identification of causal parameters in case-control and related studies. The odds ratio in the sample is our main estimand of interest and we articulate its relationship with causal parameters under various scenarios. It turns out that the odds ratio is generally a sharp upper bound for counterfactual relative risk under some monotonicity assumptions, without resorting to strong ig-norability, nor to the rare-disease assumption. Further, we propose semparametrically efficient, easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio by exploiting an explicit form of the efficient influence function. Using our new estimators, we develop methods for causal inference and illustrate the usefulness of our methods by a real-data example.
Authors
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
Pennsylvania State University
Working Paper details
- DOI
- 10.1920/wp.cem.2020.1920
- Publisher
- The IFS
Suggested citation
Jun, S and Lee, S. (2020). Causal inference in case-control studies. London: The IFS. Available at: https://ifs.org.uk/publications/causal-inference-case-control-studies (accessed: 4 November 2024).
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