Actual Causality

By Joseph Y. Halpern. 2016

399 citations recorded by the source corpus.

Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C "actually caused" event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification. Technical details are generally confined to the final section of each chapter and can be skipped by non-mathematical readers.

Subfields: Cognitive Neuroscience, History and Philosophy of Science, Causal Inference & Discovery, Philosophy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In History and Philosophy of Science

In Causal Inference & Discovery

In Philosophy

Citations in the corpus

Counterfactual Fairness (2017, neutral): “This notion is closely related to actual causes [13], or token causality in the sense that, to be fair, A should not be a cause of Ŷ in any individual instance.”
Improving the accuracy of medical diagnosis with causal machine learning (2020, background): “Despite the wealth of literature placing causal reasoning at the centre of diagnosis, to the best of our knowledge there are no existing approaches to model-based diagnosis that employ modern causal analysis techniques 49,50 .”
Eye-Tracking Causality (2017, neutral): “But we also know from previous work that a simple counterfactual contrast between what actually happened and what would have happened if the candidate cause had not been present is not sufficient (Gerstenberg & Lagnado, 2010; Halpern, 2016; Lagnado, Gerstenberg, & Zultan, 2013; Wolff, 2007; Zultan, ”
The explanation game: a formal framework for interpretable machine learning (2020, neutral): “5 For book length treatments of the topic, see Halpern (2016), Strevens (2010) and Woodward (2003).”
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality (2023, background): “…to use structural causal models (SCMs) and other formal causal models to deﬁne actual causality in a way that is consistent with how humans naturally attribute cause and related concepts of responsibility, blame, and explanations to events and their outcomes (Halpern, 2016; Kueffner, 2021).”
A Survey of the State of Explainable AI for Natural Language Processing (2020, background): “On a broader note, causality has thoroughly explored such counterfactual-based notions of explanation (Halpern, 2016).”