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

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.