Theory-Based Causal Inference
Abstract
People routinely make sophisticated causal inferences unconsciously, ef- fortlessly, and from very little data – often from just one or a few ob- servations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative mod- els of human causal judgments and brief comparisons with traditional bottom-up models of inference.
Cite
Text
Tenenbaum and Griffiths. "Theory-Based Causal Inference." Neural Information Processing Systems, 2002.Markdown
[Tenenbaum and Griffiths. "Theory-Based Causal Inference." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/tenenbaum2002neurips-theorybased/)BibTeX
@inproceedings{tenenbaum2002neurips-theorybased,
title = {{Theory-Based Causal Inference}},
author = {Tenenbaum, Joshua B. and Griffiths, Thomas L.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {43-50},
url = {https://mlanthology.org/neurips/2002/tenenbaum2002neurips-theorybased/}
}