论文标题
因果公平而选择，无知和挑战
Selection, Ignorability and Challenges With Causal Fairness
论文作者
论文摘要
在本文中，我们研究了使用因果关系反事实的流行公平方法。这些方法捕获了这样一个直观的观念，即如果预测与某人的种族，性别或宗教相反的预测相吻合，则预测是公平的。为了实现这一目标，我们必须拥有因果模型，这些模型能够捕获某人，如果我们要反法地改变这些特征。但是，我们认为，任何可以做到这一点的模型都必须在公平文献中通常考虑的特别表现阶级之外。这是因为在公平的环境中，该类别的模型需要一个特别强大的因果假设，通常仅在随机对照试验中出现。我们认为，通常这不太可能举行。此外，我们在许多情况下表明，由于从更广泛的人群中选择了样本，因此可以明确拒绝。我们表明，这给反事实公平以及应用更一般的因果公平方法的应用带来了困难。
In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the prediction that would have been made if someone's race, gender or religion were counterfactually different. In order to achieve this, we must have causal models that are able to capture what someone would be like if we were to counterfactually change these traits. However, we argue that any model that can do this must lie outside the particularly well behaved class that is commonly considered in the fairness literature. This is because in fairness settings, models in this class entail a particularly strong causal assumption, normally only seen in a randomised controlled trial. We argue that in general this is unlikely to hold. Furthermore, we show in many cases it can be explicitly rejected due to the fact that samples are selected from a wider population. We show this creates difficulties for counterfactual fairness as well as for the application of more general causal fairness methods.
