Making Things Happen: A Theory of Causal Explanation

By James F. Woodward. 2003

699 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, History and Philosophy of Science, Philosophy

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In Experimental and Cognitive Psychology

In History and Philosophy of Science

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Citations in the corpus

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “n has created a lot of debate in philosophy, but accounts of explanation both philosophical and psychology stress the importance of causality in explanation | that is, an explanation refers to causes [159, 191, 107, 59]. There are, however, denitions of non-causal explanation [52], such as explainin”
Reinforcement Learning in Healthcare: A Survey (2019, background): “The abundant literature in causal explanation [326] and inference [327] can be introduced to provide a more”
Causal inference in statistics: An overview (2009, background): “The unification of the graphical, potential outcome, structural equations, decision analytical (Dawid, 2002), interventional (Woodward, 2003), sufficient component (Rothman, 1976) and probabilistic (Suppes, 1970) approaches to causation; with each approach viewed as a restricted version of the SCM.”
Comments on Woodward, Making Things Happen (2008, background): “I welcome the chance to revisit my review of Making Things Happen and the book itself (Woodward 2003; Strevens 2007) in the light of Woodward’s response to the review (henceforth “Woodward’s reply”).”
Effective connectivity: Influence, causality and biophysical modeling (2011, background): “An important concept, stressed by Woodward, is that acauseis somethingthat “ makes things happen ” .”
No understanding without explanation (2013, background): “…such as the match- xing arrangement do feature in correct scienti c explanations—see for example Railton (1981) on the importance of the robustness of causal processes in statistical mechanics, Jackson and Pettit (1992) and Woodward (2003)—although I do not myself think that this is quite correct.)”
What Evolvability Really Is (2014, neutral): “Young et al. ([2010]) argue that the differences in limb diversity between the ape and quadrupedal monkey are the product of the relaxation of developmental constraints.”
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).”