用结构因果模型实现基于个体特征的因果推断
Individual Causal Inference with Structural Causal Model
- 提出 indiv-operator 实现从群体到个体的因果建模转化
- 定义个体化干预查询 P(Y | indiv(W), do(X), Z) 支持个性化推断
- 强调该方法关注个体可能结果而非反事实，适合个性化决策场景
个体因果推断（ICI）旨在基于个体特征预测干预效果，目标是估计随个体变化的个体因果效应（ICE）。由于数据有限且多数因果方法为群体导向，传统结构因果模型（SCM）本质上是群体层面的。然而，SCM中的外生变量（U）可编码个体差异，从而为个体化提供机制。本文提出将ICI视为因果推理的“第三层”（rung 3），通过引入 indiv(W) 算子形式化个体化过程，并定义个体因果查询 P(Y | indiv(W), do(X), Z)，实现对特定个体在假设干预下的概率推断。论证表明，ICI关注的是个体可能结果，而非非实际的反事实结果。
原文摘要 · Abstract (English)
Individual causal inference (ICI) uses causal inference methods to understand and predict the effects of interventions on individuals, considering their specific characteristics / facts. It aims to estimate individual causal effect (ICE), which varies across individuals. Estimating ICE can be challenging due to the limited data available for individuals, and the fact that most causal inference methods are population-based. Structural Causal Model (SCM) is fundamentally population-based. Therefore, causal discovery (structural learning and parameter learning), association queries and intervention queries are all naturally population-based. However, exogenous variables (U) in SCM can encode individual variations and thus provide the mechanism for individualized population per specific individual characteristics / facts. Based on this, we propose ICI with SCM as a "rung 3" causal inference, because it involves "imagining" what would be the causal effect of a hypothetical intervention on an individual, given the individual's observed characteristics / facts. Specifically, we propose the indiv-operator, indiv(W), to formalize/represent the population individualization process, and the individual causal query, P(Y | indiv(W), do(X), Z), to formalize/represent ICI. We show and argue that ICI with SCM is inference on individual alternatives (possible), not individual counterfactuals (non-actual).
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