Uncertainty in Artificial Intelligence
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Robustness of Causal Claims
Judea Pearl
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
AUTHOR = "Judea Pearl ",
TITLE = "Robustness of Causal Claims",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "446--453"

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