# Characterization of Overlap in Observational Studies

arXiv: https://arxiv.org/abs/1907.04138 · HTML: https://mlsanity.com/paper/1907.04138/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 15 of 15)

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- [β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap](https://mlsanity.com/paper/2110.05225/) (2021)
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- [Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data](https://mlsanity.com/paper/2312.10569/) (2023)
- [Case-based off-policy policy evaluation using prototype learning](https://mlsanity.com/paper/2111.11113/) (2021)
- [Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation](https://mlsanity.com/paper/2401.17737/) (2024)
- [Learning Pareto-Efficient Decisions with Confidence](https://mlsanity.com/paper/2110.09864/) (2021)
- [Prescriptive maintenance with causal machine learning](https://mlsanity.com/paper/2206.01562/) (2022)
- [Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment](https://mlsanity.com/paper/2609.01322/) (2026)
- [Refuse, Decompose, Refresh: A Claim-Safe Protocol for Closed-Loop AI Evaluation](https://mlsanity.com/paper/2609.20538/) (2026)
- [When Are Aggregate Agent Traces Diagnosable? Traffic-Governed Interpretation and Calibrated Abstention](https://mlsanity.com/paper/2609.25806/) (2026)

## Abstract (arXiv)

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.