Arbeitspapier
Causal inference in case-control studies
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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP19/20
- Classification
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Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Large Data Sets: Modeling and Analysis
Survey Methods; Sampling Methods
- Subject
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relative risk
causality
monotonicity
case-control sample
machinelearning
partial identification
semiparametric efficiency bound
- Event
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Geistige Schöpfung
- (who)
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Jun, Sung Jae
Lee, Sokbae
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2020
- DOI
-
doi:10.1920/wp.cem.2020.1920
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Jun, Sung Jae
- Lee, Sokbae
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2020
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