Robustness of Causal Claims
Artificial Intelligence
2012-07-19 v1 Methodology
Abstract
A causal claim is any assertion that invokes causal relationships between variables, for example that a drug has a certain effect on preventing a disease. Causal claims are established through a combination of data and a set of causal assumptions called a causal model. A claim is robust when it is insensitive to violations of some of the causal assumptions embodied in the model. This paper gives a formal definition of this notion of robustness and establishes a graphical condition for quantifying the degree of robustness of a given causal claim. Algorithms for computing the degree of robustness are also presented.
Cite
@article{arxiv.1207.4173,
title = {Robustness of Causal Claims},
author = {Judea Pearl},
journal= {arXiv preprint arXiv:1207.4173},
year = {2012}
}
Comments
Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)