Distilling interpretable causal trees from causal forests
This addresses the challenge of interpretability in causal inference for researchers and practitioners, though it is incremental as it builds on existing causal forest methods.
The paper tackles the problem of extracting interpretable insights from complex causal forest models by proposing Distilled Causal Trees, which distill a single, interpretable tree and outperform existing methods in noisy or high-dimensional simulations.
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.