Structure Learning in Human Causal Induction
Abstract
We use graphical models to explore the question of how people learn sim(cid:173) ple causal relationships from data. The two leading psychological theo(cid:173) ries can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayesian inference. Our argument is supported through the discussion of three data sets.
Cite
Text
Tenenbaum and Griffiths. "Structure Learning in Human Causal Induction." Neural Information Processing Systems, 2000.Markdown
[Tenenbaum and Griffiths. "Structure Learning in Human Causal Induction." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/tenenbaum2000neurips-structure/)BibTeX
@inproceedings{tenenbaum2000neurips-structure,
title = {{Structure Learning in Human Causal Induction}},
author = {Tenenbaum, Joshua B. and Griffiths, Thomas L.},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {59-65},
url = {https://mlanthology.org/neurips/2000/tenenbaum2000neurips-structure/}
}