This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via ISI Web of Science (6) Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Freedman, D. A. Search for Related Content PubMed PubMed Citation Articles by Freedman, D. A. Social Bookmarking                   What's this?

Graphical Models for Causation, and the Identification Problem

University of California, Berkeley

This article (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 thatwe 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 fewsuccessful applications of graphicalmodels, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.

Key Words: causation • linear models • graphical models • identification • invariance under intervention

Evaluation Review, Vol. 28, No. 4, 267-293 (2004)
DOI: 10.1177/0193841X04266432


CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?