Robustness of Causal Claims
This work addresses the reliability of causal inferences in fields like medicine and social sciences, representing an incremental advancement in causal analysis.
The paper tackles the problem of quantifying the robustness of causal claims to violations in underlying assumptions, and presents a formal definition, graphical condition, and algorithms for computing robustness.
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.