Distilling interpretable causal trees from causal forests

TL;DR

This paper introduces the Distilled Causal Tree, an interpretable model that extracts a single, understandable causal tree from a complex causal forest, outperforming existing methods especially in noisy or high-dimensional data.

Contribution

It presents a novel method for distilling a single interpretable causal tree from a causal forest, improving interpretability without sacrificing accuracy in complex data settings.

Findings

Outperforms existing tree extraction methods in noisy and high-dimensional data

Estimates are doubly robust and asymptotically normal

Provides clearer insights from complex causal forests

Abstract

Machine learning methods for estimating treatment effect heterogeneity promise greater flexibility than existing methods that test a few pre-specified hypotheses. However, one problem these methods can have is that it can be challenging to extract insights from complicated machine learning models. A high-dimensional distribution of conditional average treatment effects may give accurate, individual-level estimates, but it can be hard to understand the underlying patterns; hard to know what the implications of the analysis are. This paper proposes the Distilled Causal Tree, a method for distilling a single, interpretable causal tree from a causal forest. This compares well to existing methods of extracting a single tree, particularly in noisy data or high-dimensional data where there are many correlated features. Here it even outperforms the base causal forest in most simulations. Its estimates are doubly robust and asymptotically normal just as those of the causal forest are.