Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Robustness of Causal Claims
Judea Pearl
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: null
Pages: 446-453
PS Link:
PDF Link: /papers/04/p446-pearl.pdf
BibTex:
@INPROCEEDINGS{Pearl04,
AUTHOR = "Judea Pearl ",
TITLE = "Robustness of Causal Claims",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "446--453"
}


hosted by DSL   •   site info   •   help