
Causal machine learning methods like the causal forest can flexibly estimate heterogeneous treatment effects, but the black-box nature of these models poses challenges for transparency that are important to address for both usability by analysts and accountability to the public.


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estimating-heterogeneous-returns-to-education-in-australia-using-causal-machine-learning-addressing-transparency-challenges-for-usability-and-accountability


Estimating Heterogeneous Returns to Education in Australia Using Causal Machine Learning - Addressing Transparency Challenges for Usability and Accountability
