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Explanation, Invariance, and Intervention

Published online by Cambridge University Press:  01 April 2022

Jim Woodward*
Affiliation:
California Institue of Technology
*
Division of the Humanities and Social Sciences, 228-77, California Institute of Technology, Pasadena, CA 91125, jfw@hss.caltech.edu.

Abstract

This paper defends a counterfactual account of explanation, according to which successful explanation requires tracing patterns of counterfactual dependence of a special sort, involving what I call active counterfactuals. Explanations having this feature must appeal to generalizations that are invariant—stable under certain sorts of changes. These ideas are illustrated by examples drawn from physics and econometrics.

Type
Symposium: Causal Asymmetry, Intervention, and Chance
Copyright
Copyright © Philosophy of Science Association 1997

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Footnotes

Research for this paper was supported by the National Science Foundation. I am grateful to Fiona Cowie, Dave Hilbert, Alan Hajek, Kim Sterelny, and especially Nancy Cartwright and Dan Hausman for helpful discussion. I might add that the conception of explanation defended here is in many ways very similar in spirit to the conception in Hausman's forthcoming book, Causal Asymmetries, and that in particular my remarks about the explanatory significance of the econometric notion of autonomy closely parallel Hausman's ideas about the role of what he calls independent alterability in explanation.

References

Cartwright, N. (1989), Nature's Capacities and Their Measurement. Oxford: Oxford University Press.Google Scholar
Duncan, O. (1975), Introduction to Structural Equation Models. New York: Academic Press.Google Scholar
Haavelmo, T. (1944), “The Probability Approach in Econometrics”, Econometrica 12 (Supplement): 115.10.2307/1906935CrossRefGoogle Scholar
Hausman, D. (forthcoming), Causal Asymmetries.Google Scholar
Healey, R. (1992), “Causation, Robustness and EPR”, Philosophy of Science 59: 282292.10.1086/289668CrossRefGoogle Scholar
Lewis, D. (1973), “Causation”, Journal of Philosophy 70: 556567.10.2307/2025310CrossRefGoogle Scholar
Meek, C. and Glymour, C. (1994), “Conditioning and Intervening”, The British Journal for the Philosophy of Science 45: 10011021.10.1093/bjps/45.4.1001CrossRefGoogle Scholar
Papineau, D. (1993),“Can We Reduce Causal Direction to Probabilities?”, in Hull, D., Forbes, M., and Okruhlik, K. (eds.), PSA 1992, v. 2. East Lansing, MI: Philosophy of Science Association, pp. 238252.Google Scholar
Pearl, J. (1995), “Causal Diagrams for Experimental Research”, Biometrika 82: 669710.10.1093/biomet/82.4.669CrossRefGoogle Scholar
Redhead, M. (1987), Incompleteness, Nonlocality, and Realism: A Prolegomenon to the Philosophy of Quantum Mechanics. Oxford: Clarendon Press.Google Scholar
Salmon, W. (1989), Four Decades of Scientific Explanation. Minneapolis: University of Minnesota Press.Google Scholar
Spirtes, P., Glymour, C., and Scheines, R. (1993), Causation, Prediction and Search. New York: Springer-Verlag.10.1007/978-1-4612-2748-9CrossRefGoogle Scholar
Woodward, J. (forthcoming a), “Causal Independence and Faithfulness”, Multivariate Behavioral Research.Google Scholar
Woodward, J. (forthcoming b), Explanation, Invariance and Intervention.Google Scholar