Hostname: page-component-89b8bd64d-dvtzq Total loading time: 0 Render date: 2026-05-08T13:05:53.218Z Has data issue: false hasContentIssue false
  • English
  • Français

The Similarity of Causal Inference in Experimental and Non-experimental Studies

Published online by Cambridge University Press:  01 January 2022

Richard Scheines
Get access Rights & Permissions [Opens in a new window]

Abstract

For nearly as long as the word ‘correlation’ has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under which evidence accumulated from non-experimental (observational) studies can be used to infer a causal relationship. In this paper, I compare the typical conditions required to infer that one variable is a direct cause of another in observational and experimental studies. I argue that they are essentially the same.

Information

Type
Causality, Confirmation and Inference
Information
Philosophy of Science , Volume 72 , Issue 5: Proceedings of the 2004 Biennial Meeting of The Philosophy of Science Association. Part I: Contributed Papers , December 2005 , pp. 927 - 940
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bollen, K. A. (1989), Structural Equations with Latent Variables. New York: Wiley.CrossRefGoogle Scholar
Bowden, R., and Turkington, D. (1984), Instrumental Variables. New York: Cambridge University Press.Google Scholar
Cartwright, Nancy (2003), “What Is Wrong with Bayes Nets?”, in Kyburg, Henry and Thalos, Mariam (eds.), Probability Is the Very Guide of Life. Peru, IL: Open Court Press, 253–276.Google Scholar
Glymour, C., and Cooper, G. (1999), Computation, Causation, and Discovery. Menlo Park, CA: AAAI Press.Google Scholar
Hausman, D. (1998), Causal Asymmetries. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems. San Francisco: Morgan Kaufmann.Google Scholar
Pearl, J. (2000), Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.Google Scholar
Sewall, W., and Shah, V. (1968), “Social Class, Parental Encouragement, and Educational Aspirations”, Social Class, Parental Encouragement, and Educational Aspirations 73:559–572.Google Scholar
Spirtes, P., Glymour, C., and Scheines, R. (2000), Causation, Prediction, and Search. Cambridge, MA: MIT Press.Google Scholar
Spirtes, P., and Richardson, T. (1996), “A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias”, in Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics.Google Scholar
Woodward, James (2003), Making Things Happen: A Theory of Causal Explanation. Oxford: Oxford University Press.Google Scholar
Wright, S. (1934), “The Method of Path Coefficients”, The Method of Path Coefficients 5:161–215.Google Scholar