AIJan 10, 2013

Causes and Explanations: A Structural-Model Approach --- Part 1: Causes

arXiv:1301.2275v176 citations
Originality Highly original
AI Analysis

This work addresses foundational issues in causality for researchers in philosophy, AI, and statistics, offering a novel approach that is not incremental.

The authors tackled the problem of defining actual causes by proposing a new definition using structural equations to model counterfactuals, resulting in a plausible and elegant account that handles problematic examples and resolves major difficulties in traditional accounts.

We propose a new definition of actual causes, using structural equations to model counterfactuals.We show that the definitions yield a plausible and elegant account ofcausation that handles well examples which have caused problems forother definitions and resolves major difficulties in the traditionalaccount. In a companion paper, we show how the definition of causality can beused to give an elegant definition of (causal) explanation.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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