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Research questionHow can causal explanations scale to individual outcomes without abandoning counterfactual causal structure?Actual-causality analyses can require enumerating many counterfactual scenarios, making them difficult to apply beyond small models. Conventional attribution methods scale more readily but may overlook the data-generating causal structure and produce conflicting responsibility judgments.
AI
Machine Learning
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Statistical Machine Learning
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Latest papersRecent research connected to this question, newest first.A Computationally Feasible Framework for Causal Probabilistic ExplanationThe work concerns tractable estimation of graded causal explanations using probabilistic causal models and Monte Carlo approximation. Evidence covers synthetic settings, continuous-valued dynamical systems, consistency with actual-causality analyses, scaling behavior, and a deployed causal machine-learning model trained on millions of data points.research paper · Sep 3, 2026
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