Characterization of Overlap in Observational Studies. Oberst, M., Johansson, F. D., Wei, D., Gao, T., Brat, G., Sontag, D., & Varshney, K. R. In Proceedings of the Twenty-Third International Conference on Artificial Intelligence and Statistics (AISTATS) (To appear), 2020.
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Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local regions of overlap can inform the relevance of causal conclusions for new subjects, and can help guide additional data collection. To have impact, these descriptions must be interpretable for downstream users who are not machine learning experts, such as policy makers. We formalize overlap estimation as a problem of finding minimum volume sets subject to coverage constraints and reduce this problem to binary classification with Boolean rule classifiers. We then generalize this method to estimate overlap in off-policy policy evaluation. In several real-world applications, we demonstrate that these rules have comparable accuracy to black-box estimators and provide intuitive and informative explanations that can inform policy making.
@inproceedings{oberst2020overlap,
    title={Characterization of Overlap in Observational Studies},
    author={Oberst, Michael and Johansson, Fredrik D. and Wei, Dennis and Gao, Tian and Brat, Gabriel and Sontag, David and Varshney, Kush R.},
    booktitle = {Proceedings of the Twenty-Third International Conference on Artificial Intelligence and Statistics (AISTATS) (To appear)},
    year={2020},
    eprint={1907.04138},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url_Paper = {https://arxiv.org/pdf/1907.04138.pdf},
    url_Code = {https://github.com/clinicalml/overlap-code},
    abstract = {Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local regions of overlap can inform the relevance of causal conclusions for new subjects, and can help guide additional data collection. To have impact, these descriptions must be interpretable for downstream users who are not machine learning experts, such as policy makers. We formalize overlap estimation as a problem of finding minimum volume sets subject to coverage constraints and reduce this problem to binary classification with Boolean rule classifiers. We then generalize this method to estimate overlap in off-policy policy evaluation. In several real-world applications, we demonstrate that these rules have comparable accuracy to black-box estimators and provide intuitive and informative explanations that can inform policy making. }
}
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