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

2020/02/24 by Debo Cheng, Jiuyong Li, Cheng, Debo +9
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #cs.AI #stat.ME

paper · pdf · doi:10.48550/arxiv.2002.10091

12 pages,8 figures

openalex publication_date 2020/02/24 · arxiv created 2020/11/07 · arxiv updated 2020/11/10 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/28

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.

Citations

• Causal Inference Using Potential Outcomes
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• The central role of the propensity score in observational studies for causal effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Causal Effect Inference with Deep Latent-Variable Models
• Covariate selection strategies for causal inference: Classification and comparison
• Estimating individual treatment effect: generalization bounds and algorithms
• A unified survey of treatment effect heterogeneity modeling and uplift modeling

Related

• Local search for efficient causal effect estimation
• Doubly robust identification of treatment effects from multiple environments
• Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
• Combining Interventional and Observational Data Using Causal Reductions
• Confounding Feature Acquisition for Causal Effect Estimation