Linking a predictive model to causal effect estimation
将预测模型与因果效应估计相连接AI 解读 ↓
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将预测模型与因果效应估计相连接
- 研究问题
- 在个体化决策等问题中，能否让预测模型估计任意特征作为处理变量时的个体相关因果效应，而不局限于固定处理变量？
- 方法与思路
- 作者推导预测模型与干预效应之间的理论联系，提出使模型具有因果可解释性的条件，并通过实验考察效应估计及预测表现。
- 材料陈述的贡献
- 摘要称，满足文中条件的预测模型可以估计特征的因果效应，并具有一定稳健性；实验还展示了个体化决策的应用潜力。
- 阅读关注点
- 应核对具体识别条件、个体相关效应的定义及干预方式，特别关注相关性预测何时能够转换为干预概率和因果解释。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此确认同行评审或接收；摘要未列出关键条件，也不足以验证实验中的准确性比较。
原文摘要
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.