# Generalized coarsened confounding for causal effects: a large-sample framework

arXiv: https://arxiv.org/abs/2501.03129 · HTML: https://mlsanity.com/paper/2501.03129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference](https://mlsanity.com/paper/1707.06315/) (2017)
- [Machine learning for causal inference: on the use of cross-fit estimators](https://mlsanity.com/paper/2004.10337/) (2020)
- [ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data](https://mlsanity.com/paper/1609.03540/) (2016)

## Built on by (most cited first, 1 of 1)

- [Retrospective Orthogonal Design: Response-Surface Reconstruction from Observational Data](https://mlsanity.com/paper/2607.26219/) (2026)

## Abstract (arXiv)

There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding. At a high level, these procedures can be viewed as performing a clustering of confounding variables, followed by treatment effect and attendant variance estimation using the confounder strata. In addition, we propose two new algorithms for generalized coarsened confounding. While Iacus et al. (2011) developed some statistical properties for one special case in our class of procedures, we instead develop a general asymptotic framework. We provide asymptotic results for the average causal effect estimator as well as providing conditions for consistency. In addition, we provide an asymptotic justification for the variance formulae in Iacus et al. (2011). A bias correction technique is proposed, and we apply the proposed methodology to data from two well-known observational studies.