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- Singular Causation | The Oxford Handbook of Causal Reasoning | Oxford . . .
This chapter examines both philosophical accounts of the nature of singular causation, and psychological theories of people’s judgments and reasoning about singular causation
- Unifying inference and selection in singular causal explanation
In this paper we study how people give causal expla-nations when they have to jointly solve both problems We sketch a computational framework for causal explana-tion in the presence of unobserved variables, and report re-sults of an experiment testing the predictions of this model
- Causal Models - Stanford Encyclopedia of Philosophy
Causal models are mathematical models representing causal relationships within an individual system or population They facilitate inferences about causal relationships from statistical data They can teach us a good deal about the epistemology of causation, and about the relationship between causation and probability
- Time and Singular Causation—A Computational Model
Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries
- The epistemic problem of singular causation | Synthese - Springer
It has been argued that intuition, perception and mechanistic rationales generate knowledge of singular causation unambiguously supporting the metaphysical view that causation is a local relationship between individual events The analysis conducted in this paper contradicts this line of reasoning
- Introduction: The Nature of Singularist Theories of Causation - JSTOR
A theory can hold that every singular causal relation can (and even must) be accompanied by some casual law, and still be a singularist causal theory by my lights
- The role of mechanism knowledge in singular causation judgments
We here present a computational model that explains how causal mechanism information affects singular causation judgments We also use the model to identify conditions that restrict the utility of mechanism information We report three experiments testing the implications of our formal analysis
- Robust agents learn causal world models [Google DeepMind] - Reddit
We answer this question, showing that any agent capable of satisfying a regret bound under a large set of distributional shifts must have learned an approximate causal model of the data generating process, which converges to the true causal model for optimal agents
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