Towards unique and unbiased causal effect estimation from data with hidden variables

TL;DR

This paper introduces a new approach for unbiased and unique causal effect estimation from observational data with hidden variables, supported by theoretical theorems and validated through experiments on synthetic and real datasets.

Contribution

It develops theorems and algorithms for identifying proper covariate adjustment sets to achieve unbiased causal effect estimation in the presence of hidden variables.

Findings

Algorithms effectively find proper adjustment sets in synthetic and real data.

Proposed methods outperform existing bounds-based approaches.

Experiments demonstrate the approach's efficiency and practical applicability.

Abstract

Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of the causal effect of a treatment on the outcome, or generate a unique estimation of the causal effect, but making strong assumptions on data and having low efficiency. In this paper, we identify a practical problem setting and propose an approach to achieving unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications.