Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability

arXiv:2310.13240v2 Announce Type: replace
Abstract: Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, i.e., there is no globally interpretable way to understand how a model makes estimates. This is a clear problem in policy evaluation applications, particularly in government, because it is difficult to understand whether such models are functioning in ways that …

abstract accountability arxiv black boxes causal challenges cs.lg econ.em effects evaluation improving issue learning tools machine machine learning machine learning models policy tasks tools transparency treatment type usability world