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Graphical models, causal inference, and econometric models

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  • Peter Spirtes

Abstract

A graphical model is a graph that represents a set of conditional independence relations among the vertices (random variables). The graph is often given a causal interpretation as well. I describe how graphical causal models can be used in an algorithm for constructing partial information about causal graphs from observational data that is reliable in the large sample limit, even when some of the variables in the causal graph are unmeasured. I also describe an algorithm for estimating from observational data (in some cases) the total effect of a given variable on a second variable, and theoretical insights into fundamental limitations on the possibility of certain causal inferences by any algorithm whatsoever, and regardless of sample size.

Suggested Citation

  • Peter Spirtes, 2005. "Graphical models, causal inference, and econometric models," Journal of Economic Methodology, Taylor & Francis Journals, vol. 12(1), pages 3-34.
  • Handle: RePEc:taf:jecmet:v:12:y:2005:i:1:p:3-34
    DOI: 10.1080/1350178042000330887
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    Cited by:

    1. Kim, J.W. & Leatham, D.J. & Bessler, D.A., 2007. "REITs' dynamics under structural change with unknown break points," Journal of Housing Economics, Elsevier, vol. 16(1), pages 37-58, March.

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