English

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.

Keywords

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)

R2 v1 2026-06-21T21:37:26.274Z