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20222024
most citedTransparency challenges in policy evaluation with causal machine learning -- improving usability and accountability

1 citations · 1 across the 4 of their papers we have counts for

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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…