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econ.EM2024
Distilling interpretable causal trees from causal forests
Patrick Rehill
Machine learning methods for estimating treatment effect heterogeneity promise greater flexibility than existing methods that test a few pre-specified hypotheses. However, one prob…
econ.EM2023
Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
Patrick Rehill, Nicholas Biddle
Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. How…