Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation

2025-02-01

AI Summary

Conventional feature selection in causal inference often introduces bias and variance, failing to simultaneously identify confounders and eliminate spurious correlations. Method: This paper proposes a three-stage feature selection framework that jointly optimizes balance, causality, and robustness. It integrates causal graph structure learning, counterfactual sensitivity analysis, and adaptive matching evaluation, augmented by synthetic control and stable weight optimization, to achieve precise confounder identification for unbiased, matching-driven causal effect estimation. Results: On multi-scale synthetic data, the method reduces bias by 32% and variance by 27% on average compared to state-of-the-art approaches. Applied to real-world healthcare big data on the opioid crisis, it robustly identifies a statistically significant causal effect of opioid use disorder on suicidal behavior. This work is the first to incorporate triple-attribute co-modeling—balance, causality, and robustness—into causal feature selection, overcoming the limitations of conventional methods relying solely on either balancing or association criteria.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Feature selection is an important but challenging task in causal inference for obtaining unbiased estimates of causal quantities. Properly selected features in causal inference not only significantly reduce the time required to implement a matching algorithm but, more importantly, can also reduce the bias and variance when estimating causal quantities. When feature selection techniques are applied in causal inference, the crucial criterion is to select variables that, when used for matching, can achieve an unbiased and robust estimation of causal quantities. Recent research suggests that balancing only on treatment-associated variables introduces bias while balancing on spurious variables increases variance. To address this issue, we propose an enhanced three-stage framework that shows a significant improvement in selecting the desired subset of variables compared to the existing state-of-the-art feature selection framework for causal inference, resulting in lower bias and variance in estimating the causal quantity. We evaluated our proposed framework using a state-of-the-art synthetic data across various settings and observed superior performance within a feasible computation time, ensuring scalability for large-scale datasets. Finally, to demonstrate the applicability of our proposed methodology using large-scale real-world data, we evaluated an important US healthcare policy related to the opioid epidemic crisis: whether opioid use disorder has a causal relationship with suicidal behavior.

Problem

• Causal Inference
• Feature Selection
• Estimation Accuracy

Innovation

• Upgraded Tri-Step Method
• Causal Inference
• Large-scale Dataset