On specifying graphical models for causation and the identification problems Export

@inbook{freedman:2005, abstract = {This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the 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 we 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. The idea can be made more precise in several ways. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.}, author = {Freedman, David A.}, booktitle = {Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg}, chapter = {4}, citeulike-article-id = {1459525}, editor = {Andrews, Donald W. K. and Stock, James H.}, keywords = {causal\_effects, econometric\_theory, econometrics, identification}, posted-at = {2007-07-16 16:53:53}, priority = {2}, publisher = {Cambridge University Press}, title = {On specifying graphical models for causation and the identification problems}, year = {2005} }

TY - CHAP ID - freedman:2005 L3 - citeulike-article-id:1459525 N2 - This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the 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 we 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. The idea can be made more precise in several ways. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed. TI - On specifying graphical models for causation and the identification problems PB - Cambridge University Press T2 - Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg KW - causal_effects KW - econometric_theory KW - econometrics KW - identification AU - Freedman, David A2 - Andrews, Donald A2 - Stock, James PY - 2005/// ER -