# Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables

arXiv: https://arxiv.org/abs/2411.16315 · HTML: https://mlsanity.com/paper/2411.16315/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data](https://mlsanity.com/paper/2106.02881/) (2021)
- [Invariant Representation Learning for Treatment Effect Estimation](https://mlsanity.com/paper/2011.12379/) (2020)
- [Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey](https://mlsanity.com/paper/2208.09590/) (2022)
- [Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables](https://mlsanity.com/paper/2102.10324/) (2021)
- [Local Causal Structure Learning in the Presence of Latent Variables](https://mlsanity.com/paper/2405.16225/) (2024)
- [Local search for efficient causal effect estimation](https://mlsanity.com/paper/2007.10014/) (2020)
- [Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs](https://mlsanity.com/paper/2310.17816/) (2023)
- [Local Causal Discovery for Structural Evidence of Direct Discrimination](https://mlsanity.com/paper/2405.14848/) (2024)
- [Data-Driven Confounder Selection via Markov and Bayesian Networks](https://mlsanity.com/paper/1604.07212/) (2016)

## Built on by (most cited first, 3 of 3)

- [From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables](https://mlsanity.com/paper/2604.22416/) (2026)
- [Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation](https://mlsanity.com/paper/2605.06385/) (2026)
- [Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions](https://mlsanity.com/paper/2605.21548/) (2026)

## Abstract (arXiv)

Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global network structure among variables. However, identifying the global structure can be unnecessary and inefficient, especially when our primary interest lies in estimating the effect of a treatment variable on an outcome variable. To address this limitation, we propose a novel local learning approach for covariate selection in nonparametric causal effect estimation, which accounts for the presence of latent variables. Our approach leverages testable independence and dependence relationships among observed variables to identify a valid adjustment set for a target causal relationship, ensuring both soundness and completeness under standard assumptions. We validate the effectiveness of our algorithm through extensive experiments on both synthetic and real-world data.