Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
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Abstract (arXiv)
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough that many matches are created for each unit and small enough that the treatment effect is roughly constant throughout. The regions are found as either the solution to a mixed integer program, or using a (fast) approximation algorithm. The result is an interpretable and tailored estimate of a causal effect for each unit.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 1.
7 papers.
- 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
- 2021Matching for causal effects via multimarginal unbalanced optimal transportcited by 4
- 2023Variable Importance Matching for Causal Inferencecited by 4
- 2022Estimating causal effects with optimization-based methods: A review and empirical comparisoncited by 1
- 2023Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomescited by 1
- 2023Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metricscited by 1
- 2025A primer on optimal transport for causal inference with observational datacited by 0
- 2025A primer on optimal transport for causal inference with observational data10 Mar 2025
- 2023Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics3 Apr 2023
- 2023Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes24 Feb 2023
- 2023Variable Importance Matching for Causal Inference23 Feb 2023
- 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability16 Jun 2022
- 2022Estimating causal effects with optimization-based methods: A review and empirical comparison28 Feb 2022
- 2021Matching for causal effects via multimarginal unbalanced optimal transport8 Dec 2021