Corpus ID: 236956522

Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions

@inproceedings{Jun2020CausalIU,
  title={Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions},
  author={Sung Jae Jun and Sokbae (Simon) Lee},
  year={2020}
}
We study causal inference under case-control and case-population sampling. For this purpose, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risk defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual… Expand

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