Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Daniel Yarlett, Michael Ramscar
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.