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Computation and Causation
Author(s) -
Scheines Richard
Publication year - 2002
Publication title -
metaphilosophy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 35
eISSN - 1467-9973
pISSN - 0026-1068
DOI - 10.1111/1467-9973.00223
Subject(s) - causation , bayes' theorem , computer science , causal structure , interpretation (philosophy) , probabilistic logic , causal model , epistemology , class (philosophy) , bayesian network , causal inference , causality (physics) , artificial intelligence , bayesian probability , econometrics , mathematics , philosophy , statistics , physics , quantum mechanics , programming language
The computer’s effect on our understanding of causation has been enormous. By the mid‐1980s, philosophical and social‐scientific work on the topic had left us with (1) no reasonable reductive account of causation and (2) a class of statistical causal models tied to linear regression. At this time, computer scientists were attacking the problem of equipping robots with models of the external that included probabilistic portrayals of uncertainty. To solve the problem of efficiently storing such knowledge, they introduced Bayes Networks and directed graphs. By attaching a causal interpretation to Bayes Networks, the philosophy of causation changed dramatically. We are now able to be extremely general about how causal structure connects to data, and systematic about when causal structures are empirically indistinguishable. In this essay I try to motivate and describe this synthesis.