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Found 3 papers, 2 papers with code
Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using machine learning.
Policy Learning with Rare Outcomes
Machine learning (ML) estimates of conditional average treatment effects (CATE) can guide policy decisions, either by allowing targeting of individuals with beneficial CATE estimates, or as inputs to decision trees that optimise overall outcomes.
Machine learning in policy evaluation: new tools for causal inference
While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction.
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