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Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that…

Artificial Intelligence · Computer Science 2025-11-27 Joseph Y. Halpern , Rafael Pass

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with…

Artificial Intelligence · Computer Science 2023-01-10 Matej Zečević , Moritz Willig , Jonas Seng , Florian Peter Busch

Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and…

Machine Learning · Computer Science 2010-07-16 Kamran Karimi

Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often requires crucial assumptions which cannot be tested in…

Machine Learning · Computer Science 2024-02-15 Jeroen Berrevoets , Krzysztof Kacprzyk , Zhaozhi Qian , Mihaela van der Schaar

A causal claim is any assertion that invokes causal relationships between variables, for example that a drug has a certain effect on preventing a disease. Causal claims are established through a combination of data and a set of causal…

Artificial Intelligence · Computer Science 2012-07-19 Judea Pearl

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…

Artificial Intelligence · Computer Science 2013-01-14 Joseph Y. Halpern , Judea Pearl

In distributed systems where strong consistency is costly when not impossible, causal consistency provides a valuable abstraction to represent program executions as partial orders. In addition to the sequential program order of each…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-03-15 Matthieu Perrin , Achour Mostefaoui , Claude Jard

Temporal causality defines what property causes some observed temporal behavior (the effect) in a given computation, based on a counterfactual analysis of similar computations. In this paper, we study its closure properties and the…

Logic in Computer Science · Computer Science 2025-05-16 Mishel Carelli , Bernd Finkbeiner , Julian Siber

A notion of delegated causality is introduced. This subtle kind of causality is dual to interventional causality. Delegated causality elucidates the causal role of dynamical systems at the "edge of chaos", explicates evident cases of…

Adaptation and Self-Organizing Systems · Physics 2018-09-06 Raimundas Vidunas

We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, and study their complexity. We conduct an experimental…

Machine Learning · Computer Science 2020-05-26 Leopoldo Bertossi , Jordan Li , Maximilian Schleich , Dan Suciu , Zografoula Vagena

I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers Kronig relations. The specification of causality in terms of temporal differential eqations then shows us the way to write down dynamical…

Classical Physics · Physics 2018-06-04 Paul Kinsler

The possibility of non-causal signal propagation is examined for various theories of dense matter. This investigation requires a discussion of definitions of causality, together with interpretations of spacetime position. Specific examples…

High Energy Physics - Theory · Physics 2008-11-26 B. D. Keister , W. N. Polyzou

Concurrent systems identify systems, either software, hardware or even biological systems, that are characterized by sets of independent actions that can be executed in any order or simultaneously. Computer scientists resort to a causal…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-03-07 Silvia Crafa , Federica Russo

Intercausal reasoning is a common inference pattern involving probabilistic dependence of causes of an observed common effect. The sign of this dependence is captured by a qualitative property called product synergy. The current definition…

Artificial Intelligence · Computer Science 2013-03-08 Marek J. Druzdzel , Max Henrion

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of…

Physics and Society · Physics 2024-02-27 Bing Yuan , Zhang Jiang , Aobo Lyu , Jiayun Wu , Zhipeng Wang , Mingzhe Yang , Kaiwei Liu , Muyun Mou , Peng Cui

We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new insights for formulating causality as a certain risk…

Methodology · Statistics 2018-12-21 Peter Bühlmann

Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able…

Artificial Intelligence · Computer Science 2015-05-19 David Heckerman , Ross D. Shachter

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion paper. Essentially, an explanation is a…

Artificial Intelligence · Computer Science 2007-05-23 Joseph Y. Halpern , Judea Pearl

We present a precise definition of cause and effect in terms of a fundamental notion called unresponsiveness. Our definition is based on Savage's (1954) formulation of decision theory and departs from the traditional view of causation in…

Artificial Intelligence · Computer Science 2015-05-19 David Heckerman , Ross D. Shachter

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions…

Artificial Intelligence · Computer Science 2014-11-17 D. Heckerman , R. Shachter
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