3 citations · 4 across the 8 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
econ.EM2024
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…
econ.EM2024★ 3 cited
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…
econ.GN2024
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…