Toward unique and unbiased causal effect estimation from data with hidden variables
Files
(Published version)
Date
2023
Authors
Cheng, D.
Li, J.
Liu, L.
Yu, K.
Le, T.D.
Liu, J.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2023; 34(9):1-13
Statement of Responsibility
Conference Name
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 causal effects of 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 article, we identify a problem setting with the Cause Or Spouse of the treatment Only (COSO) variable assumption and propose an approach to achieving a 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.
School/Discipline
Dissertation Note
Provenance
Description
Data source: Additional data, https://doi.org/10.1109/TNNLS.2021.3133337
Access Status
Rights
Copyright 2022 IEEE.
Access Condition Notes: Accepted manuscript available open access