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- W1569440278 abstract "Publisher Summary This chapter describes the building of causal graphs from statistical data in the presence of latent variables. The problem of inferring causal relations from statistical data in the absence of experiments arises repeatedly in many scientific disciplines, including sociology, economics, epidemiology, and psychology. In addition, the building of expert systems could be expedited if background knowledge elicited from experts could be supplemented with automated techniques using relevant statistics. The efficient algorithms for determining causal relationships between random variables from appropriate statistical data when there are no unmeasured or “latent” variables are discovered. Inferring causal relations when unmeasured variables are also acting is a much more difficult problem. In many cases, it is impossible to infer the structure among the latent variables from statistical relations among the measured variables. But the presence of latent variables can also make it difficult to infer the causal relations among the measured variables themselves. The chapter discusses the spurious causal dependencies, inducing paths and graphs, and partially oriented inducing path graphs." @default.
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- W1569440278 date "1995-01-01" @default.
- W1569440278 modified "2023-09-27" @default.
- W1569440278 title "Building causal graphs from statistical data in the presence of latent variables" @default.
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- W1569440278 doi "https://doi.org/10.1016/s0049-237x(06)80075-3" @default.
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