A Computationally Feasible Framework for Causal Probabilistic Explanation

2026-09-03

AI Summary

本文提出概率因果影响(PCI)框架，通过蒙特卡洛方法估计因果模型，解决了大规模模型中因果解释的计算难题。

Abstract

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.

Problem

• Causal Probabilistic Explanation
• Actual Causality
• Scalable Attribution
• Counterfactual Scenarios
• Probabilistic Causal Model

Innovation

• Probabilistic Causal Impact (PCI)
• Monte Carlo
• Causal Explanation
• Actual Causality
• Scalability