# Local search for efficient causal effect estimation

arXiv: https://arxiv.org/abs/2007.10014 · HTML: https://mlsanity.com/paper/2007.10014/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey](https://mlsanity.com/paper/2208.09590/) (2022)
- [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)
- [Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables](https://mlsanity.com/paper/2411.16315/) (2024)
- [Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty](https://mlsanity.com/paper/2206.12689/) (2022)
- [Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference](https://mlsanity.com/paper/2312.05404/) (2023)
- [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)

Causal effect estimation from observational data is a challenging problem, especially with high dimensional data and in the presence of unobserved variables. The available data-driven methods for tackling the problem either provide an estimation of the bounds of a causal effect (i.e. nonunique estimation) or have low efficiency. The major hurdle for achieving high efficiency while trying to obtain unique and unbiased causal effect estimation is how to find a proper adjustment set for confounding control in a fast way, given the huge covariate space and considering unobserved variables. In this paper, we approach the problem as a local search task for finding valid adjustment sets in data. We establish the theorems to support the local search for adjustment sets, and we show that unique and unbiased estimation can be achieved from observational data even when there exist unobserved variables. We then propose a data-driven algorithm that is fast and consistent under mild assumptions. We also make use of a frequent pattern mining method to further speed up the search of minimal adjustment sets for causal effect estimation. Experiments conducted on extensive synthetic and real-world datasets demonstrate that the proposed algorithm outperforms the state-of-the-art criteria/estimators in both accuracy and time-efficiency.