The Propensity-Weighted Causal Learner (PWCL) for Efficient Machine Learning

Recently, much attention has been focused on the causal knowledge to enhance the predictors’ interpretability and performance in data science. This study proposes a novel propensity-weighted causal learner (PWCL) that effectively integrates causal information into the prediction by leveraging the data’s causal structure. The mathematical proofs and numerical simulation demonstrate the dual strengths of PWCL: the predictor keeps the accuracy of traditional machine learning approaches and achieves this in a fraction of the training time. In addition, a real-world scenario reveals that PWCL could potentially solve the Out-of-Distribution (OOD) problem of traditional machine learning. The findings highlight that propensity-weighted learning could be extended to a wide range of existing machine learning models to enhance performance while reducing the computational cost.