新PCI框架让复杂因果系统解释变得可行，比SHAP更尊重因果结构，比实际因果性更可扩展。
研究人员提出概率因果影响(PCI)框架，解决了实际因果性(AC)计算复杂性和归因方法忽略因果结构的问题。PCI基于实际因果理论和Pearl的必要性与充分性概率概念，将可解释性转化为概率因果模型上的估计问题。PCI在合成和真实世界数据集上进行了评估，包括与AC的一致性检查、扩展实验、复杂连续值动态系统以及基于数百万数据点训练的因果机器学习模型。
A Computationally Feasible Framework for Causal Probabilistic Explanation
Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis. Existing tools split into two camps. The theory of actual causality (AC) gives principled verdicts, but only for toy-sized models, because computing them requires enumerating counterfactual scenarios. Scalable attribution methods like SHAP (or even causal SHAP) at least partially ignore the causal structure that generated the data, and can give answers that conflict with a careful causal analysis. We close this gap with Probabilistic Causal Impact (PCI). PCI builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo. By specifying a distribution over "candidate explanations," a distribution over counterfactual values, and a scoring function, PCI provides tractable, causally grounded, graded explanations, generalizing AC and Pearl's probability of causation as degenerate cases. We evaluate PCI in synthetic and real-world examples, spanning consistency checks with AC, scaling experiments, complex continuous-valued dynamical systems, and a real-world deployed causal machine learning model trained on millions of datapoints.