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 are fair, based on the correct
interpretation of evidence and transparent enough to allow for accountability
if things go wrong. However, there has been little discussion of transparency
problems in the causal machine learning literature and how these might be
overcome. This paper explores why transparency issues are a problem for causal
machine learning in public policy evaluation applications and considers ways
these problems might be addressed through explainable AI tools and by
simplifying models in line with interpretable AI principles. It then applies
these ideas to a case-study using a causal forest model to estimate conditional
average treatment effects for a hypothetical change in the school leaving age
in Australia. It shows that existing tools for understanding black-box
predictive models are poorly suited to causal machine learning and that
simplifying the model to make it interpretable leads to an unacceptable
increase in error (in this application). It concludes that new tools are needed
to properly understand causal machine learning models and the algorithms that
fit them.

通过可解释的人工智能工具和符合可解释人工智能原则的模型简化，本研究探讨了透明度问题在公共政策评估中对因果机器学习的影响，并应用到一个案例研究中，展示了现有工具对于理解黑盒预测模型的不适用性，以及将模型简化以提高可解释性会导致误差的不可接受增加，最终得出需要新工具来正确理解因果机器学习模型及其拟合算法的结论。