A predictive model makes outcome predictions based on some given features, i.e., it estimates the conditional probability of the outcome given a feature vector. In general, a predictive model cannot estimate the causal effect of a feature on the outcome, i.e., how the outcome will change if the feature is changed while keeping the values of other features unchanged. This is because causal effect estimation requires interventional probabilities. However, many real world problems such as personalised decision making, recommendation, and fairness computing, need to know the causal effect of any feature on the outcome for a given instance. This is different from the traditional causal effect estimation problem with a fixed treatment variable. This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w.r.t. a given instance. The theoretical results naturally link a predictive model to causal effect estimations and imply that a predictive model is causally interpretable when the conditions identified in the paper are satisfied. The paper also reveals the robust property of a causally interpretable model. We use experiments to demonstrate that various types of predictive models, when satisfying the conditions identified in this paper, can estimate the causal effects of features as accurately as state-of-the-art causal effect estimation methods. We also show the potential of such causally interpretable predictive models for robust predictions and personalised decision making.

翻译：一种预测模型可以基于给定的特征进行结果预测，即估计在给定特征向量的条件下结果的条件概率。一般来说，预测模型不能对特征对结果的因果效应进行估计，即在保持其他特征值不变的情况下改变一个特征将如何改变结果。这是因为因果效应估计需要干预概率。然而，许多现实世界中的问题，比如个性化决策、推荐和公平计算，需要知道给定实例中任何特征对结果的因果效应。这与固定处理变量的传统因果效应估计问题不同。本文首先解决了关于如何估计给定实例中任何特征对结果的因果效应的难点。理论结果自然地将预测模型与因果效应估计联系起来，并暗示当本文中所述条件被满足时，预测模型是具有因果解释性的。本文还揭示了具有因果解释性的模型的鲁棒性质。我们通过实验证明，当满足本文中所述条件时，各种类型的预测模型可以像最先进的因果效应估计方法一样准确地估计特征的因果效应。我们还展示了这种因果解释性的预测模型在做出鲁棒预测和个性化决策方面的潜力。