3 citations · 4 across the 8 of their papers we have counts for
8 papers
A rapid evaluation of Australia's COVID-era apprentice wage subsidy programs
Peter Bowers, Patrick Rehill, Ethan Slaven
In the midst of the COVID-19 pandemic in 2020, the Australian Government launched two programs to incentivise new apprentices to start and complete apprenticeships -- the Boosting…
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…
How do applied researchers use the Causal Forest? A methodological review of a method
Patrick Rehill
This methodological review examines the use of the causal forest method by applied researchers across 133 peer-reviewed papers. It shows that the emerging best practice relies heav…
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…
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…
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…