Abstract
Roughly speaking, the statement “X causes Y” means that changing the value of X will change the distribution of Y. When X causes Y, X and Y will be associated but the reverse is not, in general, true. Association does not necessarily imply causation. We will consider two frameworks for discussing causation. The first uses counterfactual random variables. The second, presented in the next chapter, uses directed acyclic graphs.
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© 2004 Springer Science+Business Media New York
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Wasserman, L. (2004). Causal Inference. In: All of Statistics. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21736-9_16
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DOI: https://doi.org/10.1007/978-0-387-21736-9_16
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