@inproceedings{a5cf8ed4d85b42ce805a185e93e9c88a,
title = "The Causal Sampler: A Sampling Approach to Causal Representation, Reasoning and Learning",
abstract = "Although the causal graphical model framework has achieved success accounting for numerous causal-based judgments, a key property of these models, the Markov condition, is consistently violated (Rehder, 2014; Rehder & Davis, 2016). A new process model-the causal sampler-accounts for these effects in a psychologically plausible manner by assuming that people construct their causal representations using the Metropolis-Hastings sampling algorithm constrained to only a small number of samples (e.g., < 20). Because it assumes that Markov violations are built into people's causal representations, the causal sampler accounts for the fact that those violations manifest themselves in multiple tasks (both causal reasoning and learning). This prediction was corroborated by a new experiment that directly measured people's causal representations.",
keywords = "causal learning, causal reasoning, sampling",
author = "Davis, {Zachary J.} and Bob Rehder",
note = "Publisher Copyright: {\textcopyright} CogSci 2017.; 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 ; Conference date: 26-07-2017 Through 29-07-2017",
year = "2017",
language = "English (US)",
series = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition",
publisher = "The Cognitive Science Society",
pages = "1896--1901",
booktitle = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society",
}