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Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving reliable predictions alone is often insufficient for making informed decisions; it is equally important to understand why the treatment effect is higher for some individuals than for others. To address this two-fold challenge of prediction and interpretation, we introduce an algorithm based on decision trees and random forests for estimating individual treatment effects. Our algorithm is simple: it operates exactly like a standard random forest, bu

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