Characterization of Overlap in Observational Studies
Paper in proceeding, 2020

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.


Michael Oberst

Massachusetts Institute of Technology (MIT)

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Dennis Wei

Massachusetts Institute of Technology (MIT)

IBM Research

Tian Gao

IBM Research

Massachusetts Institute of Technology (MIT)

Gabriel Brat

Harvard Medical School

David Sontag

Massachusetts Institute of Technology (MIT)

Kush R. Varshney

Massachusetts Institute of Technology (MIT)

IBM Research

Proceedings of Machine Learning Research

26403498 (eISSN)

Vol. 108 788-797

23rd International Conference on Artificial Intelligence and Statistics (AISTATS)
, ,

Subject Categories

Information Studies

Probability Theory and Statistics

Signal Processing

More information

Latest update