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：预测模型基于给定的特征对结果进行预测，即它估计在给定特征向量条件下结果的条件概率。一般而言，预测模型无法估计特征对结果的因果效应，即在保持其他特征值不变的情况下改变该特征时结果将如何变化。这是因为因果效应估计需要干预概率。然而，许多实际问题，如个性化决策、推荐系统和公平性计算，需要知道给定实例中任意特征对结果的因果效应。这与传统以固定处理变量为对象的因果效应估计问题不同。本文首先解决了针对给定实例估计任意特征（作为处理变量）对结果因果效应的挑战。理论结果自然地将预测模型与因果效应估计联系起来，并指出当本文识别的条件满足时，预测模型具有因果可解释性。本文还揭示了因果可解释模型的鲁棒性质。我们通过实验证明，各类预测模型在满足本文识别的条件时，能够以与最先进的因果效应估计方法相当的精度估计特征的因果效应。此外，我们还展示了此类因果可解释预测模型在鲁棒预测和个性化决策中的潜力。