Statistics and Causality: Separated to Reunite—Commentary on Bryan Dowd's “Separated at Birth”
Judea Pearl,
Judea Pearl
Computer Science Department, University of California, Los Angeles, CA 90095-1596
Address correspondence to Judea Pearl, Ph.D., Professor, Computer Science Department, University of California, Los Angeles, CA 90095-1596; e-mail: [email protected]
Search for more papers by this authorJudea Pearl,
Judea Pearl
Computer Science Department, University of California, Los Angeles, CA 90095-1596
Address correspondence to Judea Pearl, Ph.D., Professor, Computer Science Department, University of California, Los Angeles, CA 90095-1596; e-mail: [email protected]
Search for more papers by this author
REFERENCES
- Ali, R., T. Richardson, and P. Spirtes. 2009. “Markov Equivalence for Ancestral Graphs. The Annals of Statistics 37: 2808–37.
- Angrist, J., and J.-S. Pischke. 2010. “The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics. Journal of Economic Perspectives 24: 3–30.
- Baron, R., and D. Kenny. 1986. “The Moderator–Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology 51: 1173–82.
- Brito, C., and J. Pearl. 2002. “ Generalized Instrumental Variables.” In Uncertainty in Artificial Intelligence, Proceedings of the Eighteenth Conference, edited by A. Darwiche and N. Friedman, pp. 85–93. San Francisco: Morgan Kaufmann.
- Campbell, D., and J. Stanley. 1963. Experimental and Quasi-Experimental Designs for Research. Chicago: Wadsworth Publishing.
- Dowd, B. 2010. “Separated at Birth: Statisticians, Social Scientists and Causality in Health Services Research. Health Services Research, this issue.
- Freedman, D. 1987. “As Others See Us: A Case Study in Path Analysis (with Discussion). Journal of Educational Statistics 12: 101–223.
- Glymour, M., and S. Greenland. 2008. “ Causal Diagrams.” In Modern Epidemiology. 3d Edition, edited by K. Rothman, S. Greenland, and T. Lash, pp. 183–209. Philadelphia, PA: Lippincott Williams & Wilkins.
- Greenland, S., J. Pearl, and J. Robins. 1999. “Causal Diagrams for Epidemiologic Research. Epidemiology 10: 37–48.
- Hafeman, D., and S. Schwartz. 2009. “Opening the Black Box: A Motivation for the Assessment of Mediation. International Journal of Epidemiology 3: 838–45.
- Heckman, J. 2010. “Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy. Journal of Economic Literature 48: 356–98.
- Holland, P. 1995. “Some Reflections on Freedman's Critiques. Foundations of Science 1: 50–7.
- Keane, M. 2010. “A Structural Perspective on the Experimentalist School. Journal of Economic Perspectives 24: 47–58.
- Kyono, T. 2010. Commentator: A Front-End User-Interface Module for Graphical and Structural Equation Modeling. Technical Report R-364, Department of Computer Science, University of California, Los Angeles, CA. Available at http://ftp.cs.ucla.edu/pub/stat_ser/r364.pdf
- Leamer, E. 2010. “Tantalus on the Road to Asymptopia. Journal of Economic Perspectives 24: 31–46.
- Nevo, A., and M. Whinston. 2010. “Taking the Dogma out of Econometrics: Structural Modeling and Credible Inference. Journal of Economic Perspectives 24: 69–82.
- Ojha, R. 2010. “A Cautionary Note on the Inclusion of Strong Predictors of the Exposure in Propensity Score Analysis.”Journal of Epidemiology and Community Health, published October 15. E-Letter Reply to, S. Cousens, J. Hargreaves, C. Bonell, B. Armstrong, J. Thomas, B. R. Kirkwood, and R. Hayes, “Alternatives to Randomisation in the Evaluation of Public-Health Interventions: Statistical Analysis and Causal Inference.”Journal of Epidemiology and Community Health Online First, Published July 13, 2010, doi:DOI: 10.1136/jech.2008.082610.
- Pearl, J. 1993. “From Bayesian Networks to Causal Networks.” In Proceedings of the Adaptive Computing and Information Processing Seminar. Brunel Conference Centre, London. See also Statistical Science, 8(3): 266–9.
- Pearl, J.. 1995. “Causal Diagrams for Empirical Research. Biometrika 82: 669–710.
- Pearl, J.. 2001. “ Direct and Indirect Effects.” In Uncertainty in Artificial Intelligence, Proceedings of the Seventeenth Conference, edited by J. S. Breese and D. Koller, pp. 411–20. San Francisco, CA: Morgan Kaufmann.
- Pearl, J. 2004. “ Robustness of Causal Claims.” In Proceedings of the Twentieth Conference Uncertainty in Artificial Intelligence, edited by M. Chickering and J. Halpern, pp. 446–53. Arlington, VA: AUAI Press.
-
Pearl, J.. 2009. Causality: Models, Reasoning, and Inference. 2d Edition. New York: Cambridge University Press.
10.1017/CBO9780511803161 Google Scholar
- Pearl, J.. 2010a. “The Foundations of Causal Inference. Sociological Methodology 40: 75–149.
- Pearl, J.. 2010b. The Mediation Formula: A Guide to the Assessment of Causal Pathways in Non-Linear Models. Technical Report R-363, Department of Computer Science, University of California, Los Angeles, CA. Available at http://ftp.cs.ucla.edu/pub/stat_ser/r363.pdf. To appear in C. Berzuini, P. Dawid, and L. Bernardinelli (Eds.), Statistical Causality. Forthcoming, 2011.
- Pearl, J.. 2010c. “On a Class of Bias-Amplifying Variables That Endanger Effect Estimates.” In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence. AUAI, Corvallis, OR, 425–32. Available at http://ftp.cs.ucla.edu/pub/stat_ser/r356.pdf
- Pearl, J., and E. Bareinboim. 2010. Transportability across Studies: A Formal Approach. Technical Report R-372, Department of Computer Science, University of California, Los Angeles, CA. Available at http://ftp.cs.ucla.edu/pub/statser/r372.pdf
- Pearl, J., and A. Paz. 2010. “ Confounding Equivalence in Causal Equivalence.” In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, edited by P. Grunwald, and P. Spirtes, pp. 433–41. Corvallis, OR: AUAI.
- Petersen, M., S. Sinisi, and M. van der Laan. 2006. “Estimation of Direct Causal Effects. Epidemiology 17: 276–84.
- Robins, J. 2001. “Data, Design, and Background Knowledge in Etiologic Inference. Epidemiology 12: 313–20.
- Rubin, D. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology 66: 688–701.
- Rubin, D.. 2004. “Direct and Indirect Causal Effects via Potential Outcomes. Scandinavian Journal of Statistics 31: 161–70.
- Rubin, D.. 2009. “Author's Reply: Should Observational Studies be Designed to Allow Lack of Balance in Covariate Distributions across Treatment Group? Statistics in Medicine 28: 1420–3.
- Rubin, D.. 2010. “Reflections Stimulated by the Comments of Shadish (2010) and West and Thoemmes (2010). Psychological Methods 15: 38–46.
- Schisterman, E., S. Cole, and R. Platt. 2009. “Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies. Epidemiology 20: 488–95.
- Shpitser, I., and J. Pearl. 2006. “ Identification of Conditional Interventional Distributions.” In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, edited by R. Dechter and T. Richardson, pp. 437–44. Corvallis, OR: AUAI Press.
- Shrier, I., and R. Platt. 2008. “Reducing Bias through Directed Acyclic Graphs. BMC Medical Research Methodology 8: doi: DOI: 10.1007/s10021-007-9035-x.
- Sobel, M. 2008. “Identification of Causal Parameters in Randomized Studies with Mediating Variables. Journal of Educational and Behavioral Statistics 33: 230–1.
- Tian, J., and J. Pearl. 2002. “ A General Identification Condition for Causal Effects.” In Proceedings of the Eighteenth National Conference on Artificial Intelligence, edited by R. Dechter, M. Kearns, and R. S. Sutton, pp. 567–73. Menlo Park, CA: AAAI Press/The MIT Press.
- Wermuth, N. 1992. “On Block-Recursive Regression Equations. Brazilian Journal of Probability and Statistics 6: 1–56.
- VanderWeele, T. 2009. “Marginal Structural Models for the Estimation of Direct and Indirect Effects. Epidemiology 20: 18–26.
- VanderWeele, T., and J. Robins. 2007. “Four Types of Effect Modification: A Classification Based on Directed Acyclic Graphs. Epidemiology 18: 561–8.
- Verma, T., and J. Pearl. 1990. “ Equivalence and Synthesis of Causal Models.” In Uncertainty in Artificial Intelligence, Proceedings of the Sixth Conference, edited by P. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, pp. 255–68. Cambridge, MA: Elsevier Science Publishers, B.V.