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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…
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…
cs.LG2022
Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation
Patrick Rehill, Nicholas Biddle
Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventi…