Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation

TL;DR

The paper introduces Causal Rule Forest (CRF), a novel interpretable model that improves heterogeneous treatment effect estimation by transforming learned patterns into Boolean rules, balancing accuracy and interpretability.

Contribution

CRF is a new approach that learns hidden data patterns and converts them into interpretable rules, enhancing both predictive accuracy and interpretability in causal inference.

Findings

CRF reduces predictive errors in HTE and CATE estimation.

CRF maintains interpretability for subgroup identification.

Experiments show CRF's potential in personalized treatment policies.

Abstract

Understanding and inferencing Heterogeneous Treatment Effects (HTE) and Conditional Average Treatment Effects (CATE) are vital for developing personalized treatment recommendations. Many state-of-the-art approaches achieve inspiring performance in estimating HTE on benchmark datasets or simulation studies. However, the indirect predicting manner and complex model architecture reduce the interpretability of these approaches. To mitigate the gap between predictive performance and heterogeneity interpretability, we introduce the Causal Rule Forest (CRF), a novel approach to learning hidden patterns from data and transforming the patterns into interpretable multi-level Boolean rules. By training the other interpretable causal inference models with data representation learned by CRF, we can reduce the predictive errors of these models in estimating HTE and CATE, while keeping their interpretability for identifying subgroups that a treatment is more effective. Our experiments underscore the potential of CRF to advance personalized interventions and policies, paving the way for future research to enhance its scalability and application across complex causal inference challenges.