Local search for efficient causal effect estimation

TL;DR

This paper introduces a fast, data-driven local search algorithm for identifying valid adjustment sets in high-dimensional observational data, enabling unbiased causal effect estimation even with unobserved variables.

Contribution

It presents a novel local search approach supported by theoretical theorems and incorporates pattern mining to efficiently find minimal adjustment sets for causal inference.

Findings

Outperforms existing methods in accuracy and speed.

Achieves unbiased causal estimates with unobserved variables.

Demonstrates effectiveness on synthetic and real datasets.

Abstract

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.