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

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 for matching algorithms but, more importantly, 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 demonstrates significant improvements in selecting the desired subset of variables compared to the state-of-the-art feature selection framework for causal inference, resulting in lower bias and variance in estimating causal quantities. We evaluated our proposed framework using state-of-the-art synthetic datasets across various settings and observed superior performance with reasonable computational cost, demonstrating scalability for large-scale datasets. Finally, to demonstrate the applicability of our proposed methodology using large-scale real-world data, we evaluated our framework on several real-world public datasets.