1 citations · 1 across the 4 of their papers we have counts for
4 papers
Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning
Patrick Rehill, Nicholas Biddle
Causal machine learning methods can be used to search for treatment effect heterogeneity in high-dimensional datasets even where we lack a strong enough theoretical framework to se…
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…
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…
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…