Structural counterfactuals: A brief introduction

Cognitive Science

Volumn 37, Issue 6, 2013, Pages 977-985

  Pearl, Judea ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Causal reasoning; Counterfactuals; Structural models

Indexed keywords

ALGORITHM; ARTICLE; CAUSAL REASONING; COUNTERFACTUALS; HUMAN; PROBLEM SOLVING; PSYCHOLOGICAL MODEL; STRUCTURAL MODELS; THINKING;

CAUSAL REASONING; COUNTERFACTUALS; STRUCTURAL MODELS;

ALGORITHMS; HUMANS; MODELS, PSYCHOLOGICAL; PROBLEM SOLVING; THINKING;

EID: 84881493299     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/cogs.12065     Document Type: Article

References (41)

1. Adams, E. (1975). The logic of conditionals. Dordrecht, The Netherlands: D. Reidel.
2. Arlo-Costa, H. (2009). The logic of conditionals. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Spring 2009 ed. http://plato.stanford.edu/archives/spr2009/entries/logic-conditionals
3. Balke, A., & Pearl, J. (1995). Counterfactuals and policy analysis in structural models. In P. Besnard & S. Hanks (Eds.), Uncertainty in artificial intelligence 11 (pp. 11-18). San Francisco, CA: Morgan Kaufmann.
4. Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182.
5. Campbell, D., & Stanley, J. (1966). Experimental and quasi-experimental designs for research. Chicago, IL: R. McNally and Co.
6. Fine, K. (1975). Review of Lewis' counterfactuals. Mind, 84, 451-458.
7. Galles, D., & Pearl, J. (1998). An axiomatic characterization of causal counterfactuals. Foundation of Science, 3(1), 151-182.
8. Gardenfors, P. (1988). Causation and the dynamics of Belief. In W. Harper & B. Skyrms (Eds.), Causation in decision, belief change and statistics II (pp. 85-104). Dordrecht, Netherlands: Kluwer Academic Publishers.
9. Goldszmidt, M., & Pearl, J. (1992). Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. In B. Nebel, C. Rich, & W. Swartout (Eds.), Proceedings of the third international conference on knowledge representation and reasoning (pp. 661-672). San Mateo, CA: Morgan Kaufmann.
10. Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11, 1-12. (Reprinted (1995) in D. F. Hendry and M. S. Morgan (Eds.), The foundations of econometric analysis (pp. 477-490). Cambridge, MA: Cambridge University Press.)
11. Hagmayer, Y., & Sloman, S.A. (2009). Decision makers conceive of themselves as interveners, not observers. Journal of Experimental Psychology: General, 138, 22-38.
12. Halpern, J. (1998). Axiomatizing causal reasoning. In G. Cooper & S. Moral (Eds.), Uncertainty in artificial intelligence (pp. 202-210), San Francisco, CA: Morgan Kaufmann. (Also (2000), Journal of Artificial Intelligence Research 12(3), 17-37.)
13. Hurwicz, L. (1950). Generalization of the concept of identification. In T. Koopmans (Ed.), Statistical inference in dynamic economic models (pp. 245-257). New York: Wiley.
14. Joyce, J. (1999). The foundatins of causal decision theory. Cambridge, MA: Cambridge University Press.
15. Joyce, J. (2009). Causal reasoning and backtracking. Philosophical Studies, 147, 139-154, 2010 (print).
16. Lewis, D. (1973). Counterfactuals and comparative probability. Journal of Philosophical Logic, 2(4), 418-446. (Reprinted (1981) in W. L. Harper, R. Stalnaker & G. Pearce (Eds.), Ifs (pp. 57-85). Dordrecht, The Netherlands: D. Reidel.)
17. Marschak, J. (1953). Economic measurements for policy and prediction. In W. C. Hood & T. Koopmans (Eds.), Studies in econometric method (pp. 1-26). New York: Wiley and Sons, Inc.
18. Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669-710.
19. Pearl, J. (2000). Causality: Models, reasoning, and inference. New York: Cambridge University Press.
20. Pearl, J. (2001). Direct and indirect effects. In J. Breese & D. Koller (Eds.), Proceedings of the seventeenth conference on uncertainty in artificial intelligence (pp. 411-420). San Francisco, CA: Morgan Kaufmann.
21. Pearl, J. (2009a). Causality: Models, reasoning, and inference, (2nd ed.). New York: Cambridge University Press.
22. Pearl, J. (2009b). Causal inference in statistics: An overview. Statistics Surveys, 3, 96-146, Available at http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf. Accessed September 2009.
23. Pearl, J. (2010). Physical and metaphysical counterfactuals (Tech. Rep. R-359, Available at http://ftp.cs.ucla.edu/pub/stat_ser/r359.pdf). Accessed January 2010. CA: Department of Computer Science, University of California, Los Angeles.
24. Pearl, J. (2012a). The mediation formula: A guide to the assessment of causal pathways in nonlinear models. In C. Berzuini, P. Dawid, & L. Bernardinelli (Eds.), Causality: Statistical perspectives and applications (pp. 151-179). Chichester, UK: John Wiley and Sons, Ltd.
25. Pearl, J. (2012b). Interpretable conditions for identifying direct and indirect effects, (Tech. Rep. R-389, Available at: http://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf). Accessed May 2013, CA: Department of Computer Science, University of California, Los Angeles.
26. Pearl, J. (2012c). Do-calculus revisited. In N. de Freitas & K. Murphy (Eds.), Proceedings of the twenty-eighth conference on uncertainty in artificial intelligence (pp. 4-11). Corvallis, OR: AUAI.
27. Pearl, J., & Bareinboim, E. (2011). Transportability of causal and statistical relations: A formal approach. In W. Burgard & D. Roth (Eds.), Proceedings of the twenty-eighth conference on artificial intelligence (AAAI-11) (pp. 247-254), Menlo Park, CA: AAAI Press. Available at: http://ftp.cs.ucla.edu/pub/stat_ser/r372a.pdf. Accessed August 2011.
28. Pearl, J., & Verma, T. (1991). A theory of inferred causation. In J. Allen, R. Fikes, & E. Sandewall (Eds.), Principles of knowledge representation and reasoning: Proceedings of the second international conference (pp. 441-452). San Mateo, CA: Morgan Kaufmann.
29. Petersen, M., Sinisi, S., & van der Laan, M. (2006). Estimation of direct causal effects. Epidemiology, 17(3), 276-284.
30. Ramsey, F. (1929). General propositions and causality. In F. P. Ramsey & H. A. Mellor (Eds.), Philosophical papers (pp. 145-153). Cambridge, MA: Cambridge University Press.
31. Robins, J., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143-155.
32. Shpitser, I., & Pearl, J. (2007). What counterfactuals can be tested. In R. Parr & L. van der Gaag (Eds.), Proceedings of the twenty-third conference on uncertainty in artificial intelligence (pp. 352-359). Vancouver, BC, Canada: AUAI Press. (Also (2008), Journal of Machine Learning Research, 9, 1941-1979.)
33. Shpitser, I., & Pearl, J. (2009). Effects of treatment on the treated: Identification and generalization. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (pp. 514-521). Arlington, VA: AUAI Press.
34. Simon, H. (1953). Causal ordering and identifiability. In W. C. Hood & T. Koopmans (Eds.), Studies in econometric method (pp. 49-74). New York: Wiley and Sons, Inc.
35. Simon, H., & Rescher, N. (1966). Cause and counterfactual. Philosophy and Science, 33, 323-340.
36. Skyrms, B. (1980). Causal necessity. New Haven, CT: Yale University Press.
37. Sloman, S., & Lagnado, D. (2005). Do we "do"? Cognitive Science, 29, 5-39.
38. Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. New York: Springer-Verlag.
39. Stalnaker, R. (1981). Letter to David Lewis. In W. Harper, R. Stalnaker, & G. Pearce (Eds.), Ifs (pp. 151-152). Dordrecht, The Netherlands: D. Reidel.
40. Taylor, C., & Dennett, D. (2011). Who's still afraid of determinism? Rethinking causes and possibilities. In R. H. Kane (Ed.), The Oxford handbook of free will (pp. 221-242). New York: Oxford University Press.
41. Tian, J., & Pearl, J. (2002). A general identification condition for causal effects. In R. Dechter, M. Kearns & R.S. Sutton (Eds.), Proceedings of the eighteenth national conference on artificial intelligence (pp. 567-573). Menlo Park, CA: AAAI Press/The MIT Press.