Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making

TL;DR

This paper explores the use of causal machine learning for policy-making, emphasizing the importance of fairness and careful modeling to avoid unintended consequences, especially considering the indirect decision-making context.

Contribution

It distinguishes between direct and indirect decision-making in causal ML applications and proposes a tailored fairness framework for policy-related models.

Findings

Standard AI Fairness methods are not suitable for causal ML in policy-making.

Policy-making involves indirect decision influence, requiring different fairness considerations.

Careful modeling and awareness of biases are crucial for fair and effective causal ML in policy contexts.

Abstract

Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelligence literature has shown, governments must be very careful of unintended consequences when using machine learning models. One way to try and protect against unintended bad outcomes is with AI Fairness methods which seek to create machine learning models where sensitive variables like race or gender do not influence outcomes. In this paper we argue that standard AI Fairness approaches developed for predictive machine learning are not suitable for all causal machine learning applications because causal machine learning generally (at least so far) uses modelling to inform a human who is the ultimate decision-maker while AI Fairness approaches assume a model that is making decisions directly. We define these scenarios as indirect and direct decision-making respectively and suggest that policy-making is best seen as a joint decision where the causal machine learning model usually only has indirect power. We lay out a definition of fairness for this scenario - a model that provides the information a decision-maker needs to accurately make a value judgement about just policy outcomes - and argue that the complexity of causal machine learning models can make this difficult to achieve. The solution here is not traditional AI Fairness adjustments, but careful modelling and awareness of some of the decision-making biases that these methods might encourage which we describe.