A Quantitative Model of Counterfactual Reasoning
Abstract
In this paper we explore two quantitative approaches to the modelling of counterfactual reasoning – a linear and a noisy-OR model – based on in- formation contained in conceptual dependency networks. Empirical data is acquired in a study and the fit of the models compared to it. We con- clude by considering the appropriateness of non-parametric approaches to counterfactual reasoning, and examining the prospects for other para- metric approaches in the future.
Cite
Text
Yarlett and Ramscar. "A Quantitative Model of Counterfactual Reasoning." Neural Information Processing Systems, 2001.Markdown
[Yarlett and Ramscar. "A Quantitative Model of Counterfactual Reasoning." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/yarlett2001neurips-quantitative/)BibTeX
@inproceedings{yarlett2001neurips-quantitative,
title = {{A Quantitative Model of Counterfactual Reasoning}},
author = {Yarlett, Daniel and Ramscar, Michael},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {123-130},
url = {https://mlanthology.org/neurips/2001/yarlett2001neurips-quantitative/}
}