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Statistical Models and Causal Inference by Jasjeet S. Sekhon, David Collier, David A. Freedman

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15On Specifying Graphical Models for Causation, and the Identification Problem

        ABSTRACT. Graphical models for causation can be set up using fewer hypothetical counterfactuals than are commonly employed. Invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions so that one can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are ...

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