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Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research

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Abstract

Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions (Glymour and Cooper, in Computation, causation, and discovery, 1999; Spirtes et al., in Causation, prediction, and search, 2000). Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning (e.g., Gopnik et al., in Psychol Rev 111:3–32, 2004) and they were used as formal models of mental causal models (e.g., Sloman, in Causal models: how we think about the world and its alternatives, 2005). A crucial assumption made by them is the Markov condition, which informally states that variables are independent of other variables that are not their direct or indirect effects conditional on their immediate causes. Whether people’s inferences conform to the causal Markov and the faithfulness condition has recently been investigated empirically. A review of respective research indicates that inferences frequently violate these conditions. This finding challenges some uses of causal Bayes nets in psychology. They entail that causal Bayes nets may not be appropriate to derive predictions for causal model theories of causal reasoning. They also question whether causal Bayes nets as a rational model are empirically descriptive. They do not challenge, however, causal Bayes nets as normative models and their usage as formal models of causal reasoning.

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Notes

  1. A set of variables is causally sufficient if there are no hidden common causes (Glymour 2001, 2003).

  2. Strictly speaking, (ii) is an implication of the CMC, while (i) is an implication of the FC. The same is true for causal chains.

  3. Strictly speaking, negative conditional dependence is a consequence of the FC in conjunction with the CMC. In addition, common-effect structures require further assumptions concerning the interaction between different causes. In general it is assumed that separate causes affect their joint effect variable independently (i.e., that there is no interaction). This entails that the influences of generative causes add.

  4. One possibility to address this problem would be define a new standard for rational models. For example, Waldmann et al. (2008) suggested a minimal rational model of causal learning, which takes into account basic principles of causality like causal directionality and precedence of cause, without making all assumptions of CBN. Such a minimal rational model would be empirically valid despite the violations of the CMC.

References

  • Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Buehner, M. J., Cheng, P. W., & Clifford, D. (2003). From covariation to causation: A test of the sssumption of causal power. Journal of Experimental Psychology: Learning, Memory, And Cognition, 29(6), 1119–1140. doi:10.1037/0278-7393.29.6.1119.

    Google Scholar 

  • Cartwright, N. (1993). Marks and probabilities: Two ways to find causal structure. In F. Stadler (Ed.), Scientific philosophy: Origins and development. Dordrecht: Kluver.

    Google Scholar 

  • Cartwright, N. (2001). What is wrong with Bayes nets? The Monist, 84, 242–264.

    Article  Google Scholar 

  • Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.

    Article  Google Scholar 

  • Glymour, C. (1998). Learning causes: Psychological explanations of causal explanation. Minds and Machines, 8, 39–60.

    Article  Google Scholar 

  • Glymour, C. (2001). The mind’s arrows: Bayes nets and graphical causal models in psychology. Cambridge, MA: MIT Press.

    Google Scholar 

  • Glymour, C. (2003). Learning, prediction and causal Bayes nets. Trends in Cognitive Science, 7, 43–48.

    Article  Google Scholar 

  • Glymour, C. (2004). We believe in freedom of the will so that we can learn. Behavioral and Brain Sciences, 27, 661–662.

    Article  Google Scholar 

  • Glymour, C., & Cooper, G. F. (1999). Computation, causation, and discovery. Boston, MA: MIT Press.

    Google Scholar 

  • Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3–32. doi:10.1037/0033-295X.111.1.3.

    Article  Google Scholar 

  • Gopnik, A., & Schulz, L. (2007). Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 334–384. doi:10.1016/j.cogpsych.2005.05.004.

    Article  Google Scholar 

  • Hagmayer, Y. (2001). Denken mit und über Kausalmodelle [Thinking with and about causal models]. Unpublished Dissertation. University of Göttingen.

  • Hagmayer, Y., & Meder, B. (2013). Repeated causal decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(1), 33–50. doi:10.1037/a0028643.

    Google Scholar 

  • Hagmayer, Y., Meder, B., Osman, M., Mangold, S., & Lagnado, D. (2010). Spontaneous causal learning while controlling a dynamic system. Open Psychology Journal, 3, 145–162.

    Google Scholar 

  • Hagmayer, Y., & Sloman, S. A. (2009). Decision makers conceive of their choices as interventions. Journal of Experimental Psychology: General, 138(1), 22–38. doi:10.1037/a0014585.

    Article  Google Scholar 

  • Hagmayer, Y., & Waldmann, M. R. (2007). Inferences about unobserved causes in human contingency learning. Quarterly Journal of Experimental Psychology, 60, 330–355. doi:10.1080/17470210601002470.

    Article  Google Scholar 

  • Hausman, D. M., & Woodward, J. (1999). Independence, invariance, and the causal Markov condition. British Journal for the Philosophy of Science, 50, 521–583.

    Article  Google Scholar 

  • Kelley, H. H. (1972). Causal schemata and the attribution process. In E. E. Jones, D. E. Knaouse, H. H. Kelley, R. E. Nisbett, S. Valins, & B. Weiner (Eds.), Attribution, perceiving the causes of behavior. Morristown, NJ: General Learning Press.

    Google Scholar 

  • Kelley, H. H., & Michaela, J. L. (1980). Attribution theory and research. Annual Review of Psychology, 31, 457–501. doi:10.1146/annurev.ps.31.020180.002325.

    Article  Google Scholar 

  • Krynski, T. R., & Tenenbaum, J. B. (2007). The role of causality in judgment under uncertainty. Journal of Experimental Psychology: General, 136(3), 430–450. doi:10.1037/0096-3445.136.3.430.

    Article  Google Scholar 

  • Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation: Cues to causal structure. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 154–172). Oxford: Oxford University Press. doi:10.1093/acprof:oso/9780195176803.003.0011.

    Chapter  Google Scholar 

  • Luhmann, C. C., & Ahn, W. (2007). BUCKLE: A model of unobserved cause learning. Psychological Review, 114, 657–677. doi:10.1037/0033-295X.114.3.657.

    Article  Google Scholar 

  • Marr, D. (1982). Vision: A computational investigation into human representation and processing of visual information. San Diego, CA: Freeman.

    Google Scholar 

  • Mayrhofer, R., Goodman, N. D., Waldmann, M. R., & Tenenbaum, J. B. (2008). Structured correlation from the causal background. In Proceedings of the thirtieth annual conference of the Cognitive Science Society (pp. 303–308). Austin, TX: Cognitive Science Society.

  • Mayrhofer, R., Hagmayer, Y., & Waldmann, M. R. (2010). Agents and causes: A Bayesian error attribution model of causal reasoning. In R. Camtrabone & S. Ohlsson (Eds.), Proceedings of the thirty-second annual conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

    Google Scholar 

  • Mayrhofer, R., & Waldmann, M. R. (2014). Agents and causes: Dispositional intuitions as a guide to causal structure. Cognitive Science, 38, 1–31. doi:10.1111/cogs.12132.

    Article  Google Scholar 

  • Morris, M. W., & Larrick, R. P. (1995). When one cause casts doubt on another: A normative analysis of discounting in causal attribution. Psychological Review, 102, 331–355. doi:10.1037/0033-295X.102.2.331.

    Article  Google Scholar 

  • Park, J., & Sloman, S. A. (2013). Mechanistic beliefs determine adherence to the Markov property in causal reasoning. Cognitive Psychology, 67, 186–216. doi:10.1016/j.cogpsych.2013.09.002.

    Article  Google Scholar 

  • Park, J., & Sloman, S. A. (2014). Causal explanation in the face of contradiction. Memory & Cognition, 42, 806–820. doi:10.3758/s13421-013-0389-3.

    Article  Google Scholar 

  • Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann Publishers.

    Google Scholar 

  • Perales, J., Catena, A., & Maldonado, A. (2004). Inferring non-observed correlations from causal scenarios: The role of causal knowledge. Learning and Motivation, 35, 115–135. doi:10.1016/S0023-9690(03)00042-0.

    Article  Google Scholar 

  • Rehder, B. (2006). Human deviations from normative causal reasoning. Poster presented at the 28th annual conference of the Cognitive Science Society, Vancouver, British Columbia, Canada.

  • Rehder, B. (2014a). The role of functional form in causal-based categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition. doi:10.1037/xlm0000048.

  • Rehder, B. (2014). Independence and non-independence in human causal reasoning. Cognitive Psychology, 72, 54–107. doi:10.1016/j.cogpsych.2014.02.002.

    Article  Google Scholar 

  • Rehder, B., & Burnett, R. C. (2005). Feature inference and the causal structure of categories. Cognitive Psychology, 50, 264–314. doi:10.1016/j.cogpsych.2004.09.002.

    Article  Google Scholar 

  • Rehder, B., & Hastie, R. (2001). Causal knowledge and categories: The effect of causal beliefs on categorization, induction, and similarity. Journal of Experimental Psychology: General, 130, 323–360. doi:10.1037/0096-3445.130.3.323.

    Article  Google Scholar 

  • Rehder, B., & Kim, S. (2010). Causal status and coherence in causal-based categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 1171–1206.

    Google Scholar 

  • Rottman, B. M., & Hastie, R. (2014). Reasoning about causal relationships: Inferences on causal networks. Psychological Bulletin, 140, 109–139. doi:10.1037/a0031903.

    Article  Google Scholar 

  • Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Sloman, S. A. (2005). Causal models: How we think about the world and its alternatives. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Sloman, S. A., & Lagnado, D. A. (2005). Do we “do”? Cognitive Science, 29, 5–39. doi:10.1207/s15516709cog2901-2.

    Article  Google Scholar 

  • Spellman, B. A. (1996). Acting as intuitive scientists: Contingency judgments are made while controlling for alternative potential causes. Psychological Science, 7, 337–342.

    Article  Google Scholar 

  • Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. Cambridge: MIT Press.

  • Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. Cambridge: MIT Press.

  • Steyvers, M., Tenenbaum, J. B., Wagenmakers, E.-J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453–489. doi:10.1207/s15516709cog2703-6.

    Article  Google Scholar 

  • Sussman, A. B., & Oppenheimer, D. (2011). A causal model theory of judgment. In C. Hölscher, L. Carlson, & T. Shipley (Eds.), Proceedings of the 33rd annual conference of the Cognitive Science Society (pp. 1703–1708). Austin, TX: Cognitive Science Society.

    Google Scholar 

  • Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285.

    Article  Google Scholar 

  • Waldmann, M. R. (1996). Knowledge-based causal induction. In D. R. Shanks, K. L. Holyoak, & D. L. Medin (Eds.), The Psychology of Learning and Motivation (Vol. 34, pp. 47–88). San Diego, CA: Academic Press.

    Google Scholar 

  • Waldmann, M. R., Cheng, P. W., Hagmayer, Y., & Blaisdell, A. P. (2008). Causal learning in rats and humans: a minimal rational model. In N. Chater & M. Oaksford (Eds.), The probabilistic mind. Prospects for Bayesian cognitive science (pp. 453–484). Oxford: University Press.

    Chapter  Google Scholar 

  • Waldmann, M. R., & Hagmayer, Y. (2001). Estimating causal strength: The role of structural knowledge and processing effort. Cognition, 82, 27–58.

    Article  Google Scholar 

  • Waldmann, M. R., & Hagmayer, Y. (2005). Seeing versus doing: Two modes of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 216–227. doi:10.1037/0278-7393.31.2.216.

    Google Scholar 

  • Waldmann, M. R., & Holyoak, K. J. (1992). Predictive and diagnostic learning within causal models: Asymmetries in cue competition. Journal of Experimental Psychology: General, 121, 222–236.

    Article  Google Scholar 

  • Waldmann, M. R., Holyoak, K. J., & Fratianne, A. (1995). Causal models and the acquisition of category structure. Journal of Experimental Psychology: General, 124, 181–206.

    Article  Google Scholar 

  • Walsh, C. R., & Sloman, S. A. (2004). Revising causal beliefs. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th annual conference of the Cognitive Science Society (pp. 1423–1427). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Walsh, C. R., & Sloman, S. A. (2007). Updating beliefs with causal models: Violations of screening off. In M. A. Gluck, J. R. Anderson, & S. M. Kosslyn (Eds.), Memory and mind: A festschrift for Gordon H. Bower (pp. 345–358). New York, NY: Erlbaum.

    Google Scholar 

  • White, P. A. (2006). The causal asymmetry. Psychological Review, 113, 132–147.

    Article  Google Scholar 

  • Wolff, P. (2007). Representing causation. Journal of Experimental Psychology: General, 136, 82–111.

    Article  Google Scholar 

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  1. Georg-Elias-Mueller-Institute for Psychology, University of Goettingen, Gosslerstr. 14, 37073, Goettingen, Germany

    York Hagmayer

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Hagmayer, Y. Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research. Synthese 193, 1107–1126 (2016). https://doi.org/10.1007/s11229-015-0734-0

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