Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability
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政策评估中因果机器学习的透明性挑战：可用性与问责
- 研究问题
- 政策评估使用黑箱因果机器学习时，如何理解其处理效应估计，并满足证据解释、公平性和责任追溯的要求？
- 方法与思路
- 作者讨论可解释 AI 工具和模型简化，并以澳大利亚假设性离校年龄政策变化的因果森林条件平均处理效应估计为案例。
- 材料陈述的贡献
- 摘要称现有预测模型解释工具不适合该因果学习应用，模型简化又带来不可接受的误差增加，因此需要新的解释工具。
- 阅读关注点
- 应核对解释对象究竟是结局预测还是处理效应、误差评价标准，以及案例如何操作化透明性、公平性和问责要求。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未报告误差数值，该案例结论不能直接推广到所有模型。
原文摘要
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.