Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A graphical criterion for the identification of causal effects in linear models
Eighteenth national conference on Artificial intelligence
Generalized instrumental variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
An epistemological comparison between fuzzy logic engines and Bayesian filters
WSEAS Transactions on Systems and Control
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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.