Robustness of Causal Claims

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

Text

Pearl. "Robustness of Causal Claims." Conference on Uncertainty in Artificial Intelligence, 2004.

Markdown

[Pearl. "Robustness of Causal Claims." Conference on Uncertainty in Artificial Intelligence, 2004.](https://mlanthology.org/uai/2004/pearl2004uai-robustness/)

BibTeX

@inproceedings{pearl2004uai-robustness,
  title     = {{Robustness of Causal Claims}},
  author    = {Pearl, Judea},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2004},
  pages     = {446-453},
  url       = {https://mlanthology.org/uai/2004/pearl2004uai-robustness/}
}