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Interventions and Causal Inference

Published online by Cambridge University Press:  01 January 2022

Frederick Eberhardt  and
Richard Scheines
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Abstract

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard’ and ‘soft’ interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the particular experimental setup and the assumptions that can be made.

Type
Philosophy of Science: Causation
Information
Philosophy of Science , Volume 74 , Issue 5: Proceedings of the 2006 Biennial Meeting of the Philosophy of Science Association. Part I: Contributed Papers. Edited by Cristina Bicchieri and Jason Alexander , December 2007 , pp. 981 - 995
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

The first author is funded by the Causal Learning Collaborative Initiative supported by the James S. McDonnell Foundation. Many aspects of this paper were inspired by discussions with members of the collaborative.

References

Campbell, J. (2007), “An Interventionist Approach to Causation in Psychology”, in Gopnik, A. and Schulz, L. (eds.), Causal Learning: Psychology and Computation. Oxford: Oxford University Press.Google Scholar
Cartwright, Nancy (2001), “What is Wrong with Bayes Nets?”, What is Wrong with Bayes Nets? 16:242–264.Google Scholar
Cartwright, Nancy (2002), “Against Modularity, the Causal Markov Condition and Any Link between the Two”, Against Modularity, the Causal Markov Condition and Any Link between the Two 53:411–453.Google Scholar
Cartwright, Nancy (2006), “From Metaphysics to Method: Comments on Manipulability and the Causal Markov Condition”, From Metaphysics to Method: Comments on Manipulability and the Causal Markov Condition 57:197–218.Google Scholar
Eberhardt, Frederick, Glymour, Clark, and Scheines, Richard (2005), “On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations among N Variables”, in Bacchus, Fahiem and Jaakkola, Tommi (eds.), Proceedings of the 21st Conference on Uncertainty and Artificial Intelligence. Corvallis, OR: AUAI Press, 178–184.Google Scholar
Eberhardt, Frederick, Glymour, Clark, and Scheines, Richard (2006), “N−1 Experiments Suffice to Determine the Causal Relations among N Variables”, in Holmes, Dawn E. and Jain, Lakhmi C. (eds.), Innovations in Machine Learning, Theory and Applications Series: Studies in Fuzziness and Soft Computing. Vol. 194. New York: Springer-Verlag.Google Scholar
Fisher, Ronald A. (1935), The Design of Experiments. New York: Hafner.Google Scholar
Hausman, Dan, and Woodward, James (1999), “Independence, Invariance, and the Causal Markov Condition”, Independence, Invariance, and the Causal Markov Condition 50:521–583.Google Scholar
Hausman, Dan, and Woodward, James (2004), “Modularity and the Causal Markov Condition: A Restatement”, Modularity and the Causal Markov Condition: A Restatement 55:147–161.Google Scholar
Hitchcock, Chris (2007), “On the Importance of Causal Taxonomy”, in Gopnik, Alison and Schulz, Laura (eds.), Causal Learning: Psychology, Philosophy and Computation. New York: Oxford University Press, 107–116.Google Scholar
Hoover, Kevin (2003), “Nonstationary Time Series, Cointegration, and the Principle of Common Cause”, Nonstationary Time Series, Cointegration, and the Principle of Common Cause 54:527–551.Google Scholar
Korb, Kevin, Hope, Lucas, Nicholson, Ann, and Axnick, Karl (2004), “Varieties of Causal Intervention”, in Proceedings of the Pacific Rim International Conference on AI. New York: Springer.Google Scholar
Pearl, Judea (2000). Causality. Oxford: Oxford University Press.Google Scholar
Sober, Elliott (2001), “Venetian Sea Levels, British Bread Prices, and the Principle of Common Cause”, Venetian Sea Levels, British Bread Prices, and the Principle of Common Cause 52:331–346.Google Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard (2000), Causation, Prediction and Search. Springer Lecture Notes in Statistics. Cambridge, MA: MIT Press.Google Scholar
Steel, Dan (2005), “Indeterminism and the Causal Markov Condition”, Indeterminism and the Causal Markov Condition 56:3–26.Google Scholar
Woodward, James (2003), Making Things Happen. Oxford: Oxford University Press.Google Scholar