| Tian, J. and J. Pearl (2000). Probabilities of Causation: Bounds and Identification. Annals of Mathematics and Artificial Intelligence 28, 287-313. |
.... 1986] and noncollapsibility [Hauck et al. 1991, Grayson 1987] and between probability of causation [Greenland and Robins, 1988] and attributable fractions [Schlesselman, 1982] have taken heavy tolls before they were finally resolved [Greenland et al. 1999b; Pearl 2000, Chapter 6; Greenland 1999; Tian and Pearl 2000]. 7 Commenting on my set(x) notation [Pearl, 1995a, b] a leading statistician wrote: Is this a concept in some new theory of probability or expectation If so, please provide it. Otherwise, metaphysics may remain the leading explanation. Another statistician, commenting on the do(x) ....
.... other hand, the algebraic machinery offered by the potential outcome notation, once a problem is properly formalized, can be extremely powerful in refining assumptions [Angrist et al. 1996] deriving consistent estimands [Robins, 1986] bounding probabilities of necessary and sufficient causation [Tian and Pearl, 2000], and combining data from experimental and nonexperimental studies. The next section presents a way that should help researchers combine the best features of the two approaches. It is based on encoding causal assumptions in the language of diagrams, translating these assumptions into potential ....
J. Tian and J. Pearl. Probabilities of causation: Bounds and identification. In Proceedings of the Sixtheenth Conference on Uncertainty in Artificial Intelligence, pages 589--598. Morgan Kaufmann, San Francisco, CA, 2000.
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Tian, J. and J. Pearl (2000). Probabilities of Causation: Bounds and Identification. Annals of Mathematics and Artificial Intelligence 28, 287-313.
No context found.
Tian, J. and J. Pearl (2000). Probabilities of Causation: Bounds and Identification. Annals of Mathematics and Artificial Intelligence 28, 287-313.
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