Portable Causal Dependence: A Tale of Consilience
Abstract
This article describes research pursued by members of the McDonnell Collaborative on Causal Learning. A number of members independently converged on a similar idea: one of the central functions served by claims of actual causation is to highlight patterns of dependence that are highly portable into novel contexts. I describe in detail how this idea emerged in my own work and also in that of the psychologist Tania Lombrozo. In addition, I use the occasion to reflect on the nature of interdisciplinary collaboration in general and on the interaction between philosophy and psychology in particular.
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We are particularly interested in causal explanations because they provide us with reliable means to affect and predict our surroundings.
“Philosophical and empirical research has converged on the thesis that we are particularly interested in causal explanations because they provide us with reliable means to affect and predict our surroundings”
From AI, Opacity, and Personal Autonomy (2022) · cites this paper as Hitchcock, 2012
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Normality and actual causal strength
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AI, Opacity, and Personal Autonomy
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“The second way to introduce gradations of causal judgments involves a complementary causal condition: robust sufficiency (Lewis, 1973; Pearl, 1999; Woodward, 2006; Lombrozo, 2010; Hitchcock, 2012; Kominsky et al., 2015; Icard et al., 2017; Usher, 2018; Vasilyeva et al., 2018).”
Background
- Authors
- Hitchcock, C.
- Corresponding author
- Hitchcock, Christopher
- Venue
- Philosophy of Science
- Publisher
- Cambridge University Press (CUP)
- Published
- 2012-12
- Volume
- 79
- Issue
- 5
- Pages
- 942-951
- ISSN
- 0031-8248 (ISSN-L)
- 1539-767X
- OpenAlex
- W2172378643
- Cited by
- 32
- References
- 28
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 8 · 2021: 5 · 2020: 5 · 2019: 1 · 2018: 1 · 2017: 3 · 2015: 1 · 2014: 1 · 2012: 1
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Philosophy of Science (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Keywords
- actual causation, causal dependencies, causal learning, interdisciplinary collaboration, philosophy of psychology, philosophy of mind
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