# Variable Importance Matching for Causal Inference

arXiv: https://arxiv.org/abs/2302.11715 · HTML: https://mlsanity.com/paper/2302.11715/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Double/Debiased Machine Learning for Treatment and Causal Parameters](https://mlsanity.com/paper/1608.00060/) (2016)
- [Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects](https://mlsanity.com/paper/1706.09523/) (2017)
- [Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations](https://mlsanity.com/paper/1309.4686/) (2013)
- [Validating Causal Inference Methods](https://mlsanity.com/paper/2202.04208/) (2022)
- [FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference](https://mlsanity.com/paper/1707.06315/) (2017)
- [MALTS: Matching After Learning to Stretch](https://mlsanity.com/paper/1811.07415/) (2018)
- [Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge](https://mlsanity.com/paper/1907.07592/) (2019)
- [Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation](https://mlsanity.com/paper/2003.01805/) (2020)
- [A Framework for Optimal Matching for Causal Inference](https://mlsanity.com/paper/1606.05188/) (2016)

## Built on by (most cited first, 4 of 4)

- [Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data](https://mlsanity.com/paper/2602.14274/) (2026)
- [Leo Breiman, the Rashomon Effect, and the Occam Dilemma](https://mlsanity.com/paper/2507.03884/) (2025)
- [A Two-Stage Interpretable Matching Framework for Causal Inference](https://mlsanity.com/paper/2504.09635/) (2025)
- [DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments](https://mlsanity.com/paper/2605.06608/) (2026)

## Abstract (arXiv)

Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data. We describe a general framework called Model-to-Match that achieves these goals by (i) learning a distance metric via outcome modeling, (ii) creating matched groups using the distance metric, and (iii) using the matched groups to estimate treatment effects. Model-to-Match uses variable importance measurements to construct a distance metric, making it a flexible framework that can be adapted to various applications. Concentrating on the scalability of the problem in the number of potential confounders, we operationalize the Model-to-Match framework with LASSO. We derive performance guarantees for settings where LASSO outcome modeling consistently identifies all confounders (importantly without requiring the linear model to be correctly specified). We also provide experimental results demonstrating the method's auditability, accuracy, and scalability as well as extensions to more general nonparametric outcome modeling.