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cs.LG2023★ 1 cited
Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability
Patrick Rehill, Nicholas Biddle
Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machin…
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…
cs.LG2023
Counterfactually Fair Regression with Double Machine Learning
Patrick Rehill
Counterfactual fairness is an approach to AI fairness that tries to make decisions based on the outcomes that an individual with some kind of sensitive status would have had withou…