# MALTS: Matching After Learning to Stretch

arXiv: https://arxiv.org/abs/1811.07415 · HTML: https://mlsanity.com/paper/1811.07415/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [Validating Causal Inference Methods](https://mlsanity.com/paper/2202.04208/) (2022)
- [Variable Importance Matching for Causal Inference](https://mlsanity.com/paper/2302.11715/) (2023)
- [Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects](https://mlsanity.com/paper/2403.11332/) (2024)
- [Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data](https://mlsanity.com/paper/2312.10569/) (2023)
- [Estimating causal effects with optimization-based methods: A review and empirical comparison](https://mlsanity.com/paper/2203.00097/) (2022)
- [Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design](https://mlsanity.com/paper/2210.06825/) (2022)
- [Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics](https://mlsanity.com/paper/2304.01316/) (2023)
- [Safe and Interpretable Estimation of Optimal Treatment Regimes](https://mlsanity.com/paper/2310.15333/) (2023)
- [Leo Breiman, the Rashomon Effect, and the Occam Dilemma](https://mlsanity.com/paper/2507.03884/) (2025)
- [dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference](https://mlsanity.com/paper/2101.01867/) (2021)
- [A Two-Stage Interpretable Matching Framework for Causal Inference](https://mlsanity.com/paper/2504.09635/) (2025)
- [Revisiting Broken Windows Theory](https://mlsanity.com/paper/2509.16490/) (2025)
- [A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching](https://mlsanity.com/paper/2604.27307/) (2026)

## Abstract (arXiv)

We introduce a flexible framework that produces high-quality almost-exact matches for causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to poor quality matches, particularly when there are irrelevant covariates. In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches. The learned distance metric stretches the covariate space according to each covariate's contribution to outcome prediction: this stretching means that mismatches on important covariates carry a larger penalty than mismatches on irrelevant covariates. Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.