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Causal Induction
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(Click on an author's name to view all papers by that author.)
Causal Induction | |
| Pacer, M., Williams, J., Xi, C., Lombrozo, T., & Griffiths, T. L. (in press). Evaluating computational models of explanation using human judgments. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. (pdf) |
| Hu, J. C., Buchsbaum, D., Griffiths, T. L., & Xu, F. (in press). When does the majority rule? Preschoolers' trust in majority informants varies by task domain. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf) |
| Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (in press). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review. (pdf) |
| Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (in press). Rational variability in children's causal inferences: The Sampling Hypothesis. Cognition. (doi) |
| Buchsbaum, D., Bridgers, S., Whalen, A., Seiver, E., Griffiths, T. L., & Gopnik, A. (2012). Do I know that you know what you know? Modeling testimony in causal inference. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf) |
| Pacer, M., & Griffiths, T. L. (2012). Elements of a rational framework for continuous-time causal induction. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf) |
| Griffiths, T. L., Sobel, D., Tenenbaum, J. B., & Gopnik, A. (2011). Bayes and blickets: Effects of knowledge on causal induction in children and adults. Cognitive Science, 35, 1407-1455. (pdf) |
| Pacer, M., & Griffiths, T. L. (2011). A rational model of causal induction with continuous causes. Advances in Neural Information Processing Systems, 24. (pdf) |
| Buchsbaum, D., Gopnik, A., Griffiths, T. L., & Shafto, P. (2011). Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence. Cognition, 120, 331-340. (pdf) |
| Yeung, S., & Griffiths, T. L. (2011). Estimating human priors on causal strength. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf) |
| Abbott, J. T., & Griffiths, T. L. (2011). Exploring the influence of particle filter parameters on order effects in causal learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf) |
| Bonawitz, E., Denison, S., Chen, A., Gopnik, A., & Griffiths, T. L. (2011). A simple sequential algorithm for approximating Bayesian inference. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf) |
| Buchsbaum, D., Gopnik, A., & Griffiths, T. L. (2010). Children's imitation of action sequences is influenced by statistical evidence and inferred causal structure. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf) |
| Bonawitz, E. B., & Griffiths, T. L. (2010). Deconfounding hypothesis generation and evaluation in Bayesian models. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf) |
| Denison, S., Bonawitz, E. B., Gopnik, A., & Griffiths, T. L. (2010). Preschoolers sample from probability distributions. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf) |
| Lucas, C. G., Gopnik, A., & Griffiths, T. L. (2010). Developmental differences in learning the forms of causal relationships. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf) |
| Kemp, C., Tenenbaum, J.B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165-196. (doi) |
| Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science, 34, 113-147. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116, 661-716. (pdf) |
| Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2009). A Bayesian framework for modeling intuitive dynamics. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf) |
| Buchsbaum, D., Griffiths, T. L., Gopnik, A., & Baldwin, D. (2009). Learning from actions and their consequences: Inferring causal variables from continuous sequences of human action. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf) |
| Griffiths, T. L., Lucas, C., Williams, J. J., & Kalish, M. L. (2009). Modeling human function learning with Gaussian processes. Advances in Neural Information Processing Systems 21. (pdf) |
| Schulz, L.E., Bonawitz, E. B., & Griffiths, T. L. (2007). Can being scared make your tummy ache? Naive theories, ambiguous evidence and preschoolers' causal inferences. Developmental Psychology, 43, 1124-1139. (pdf) |
| Tenenbaum, J. B., Griffiths, T. L., & Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In A. Gopnik, & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2007). Two proposals for causal grammars. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford: Oxford University Press. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226. (pdf) |
| Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309-318. (pdf) |
| Bonawitz, E. B., Griffiths, T. L., & Schulz, L. (2006). Modeling cross-domain causal learning in preschoolers as Bayesian inference. Proceedings of the 28th Annual Conference of the Cognitive Science Society. (pdf) (winner of the Marr Prize for best student paper) |
| Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf) |
| Wood, F., Griffiths, T. L., & Ghahramani, Z. (2006). A non-parametric Bayesian method for inferring hidden causes. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384. (pdf) (Matlab code for computing causal support) |
| Griffiths, T. L. (2005). Causes, coincidences, and theories. Unpublished doctoral dissertation, Stanford University, Stanford CA. (pdf) |
| Kemp, C., Griffiths, T. L., & Tenenbaum, J. B. (2004). Discovering latent classes in relational data. AI Memo 2004-019 (pdf) |
| Griffiths, T. L., Baraff, E.R., & Tenenbaum, J. B. (2004). Using physical theories to infer hidden causal structure. Proceedings of the 26th Annual Conference of the Cognitive Science Society. (pdf) |
| Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. Advances in Neural Information Processing Systems 15. (pdf) |
| Tenenbaum, J. B., & Griffiths, T. L. (2003). Theory-based causal inference. Advances in Neural Information Processing Systems 15. (pdf) |
| Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. Advances in Neural Information Processing Systems 13. (pdf) (Matlab code for computing causal support) |