A Computationally Feasible Framework for Causal Probabilistic Explanation
Quick summary
arXiv:2609.04177v1 Announce Type: new 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.
Key takeaways
- arXiv:2609.04177v1 Announce Type: new 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.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “A Computationally Feasible Framework for Causal Probabilistic Explanation” may reshape data collection, model training, output accountability and market access.
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