1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Fast Learning of Optimal Policy Trees
James Cussens, Julia Hatamyar, Vishalie Shah +1
We develop and implement a version of the popular "policytree" method (Athey and Wager, 2021) using discrete optimisation techniques. We test the performance of our algorithm in fi…
Learning control variables and instruments for causal analysis in observational data
Nicolas Apfel, Julia Hatamyar, Martin Huber +1
This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome…
Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
Julia Hatamyar, Noemi Kreif, Rudi Rocha +1
We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adopti…
Policy Learning with Rare Outcomes
Julia Hatamyar, Noemi Kreif
Machine learning (ML) estimates of conditional average treatment effects (CATE) can guide policy decisions, either by allowing targeting of individuals with beneficial CATE estimat…