David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Journal of Economic Methodology 12 (1):3-34 (2005)
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
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Judea Pearl (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
Peter Spirtes, Clark Glymour & Richard Scheines (1996). Causation, Prediction, and Search. British Journal for the Philosophy of Science 47 (1):113-123.
Kevin D. Hoover (2001). Causality in Macroeconomics. Monograph Collection (Matt - Pseudo).
Kevin D. Hoover (2003). Nonstationary Time Series, Cointegration, and the Principle of the Common Cause. British Journal for the Philosophy of Science 54 (4):527-551.
Citations of this work BETA
No citations found.
Similar books and articles
T. Chu & D. Danks, Data Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections: Climate Teleconnection Mechanisms.
Clark Glymour, Data Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections: Climate Teleconnection Mechanisms.
Peter Spirtes, Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.
Richard Scheines, Clark Glymour & Peter Spirtes, Learning the Structure of Linear Latent Variable Models.
Federica Russo (2009). Causal Arrows in Econometric Models. Humana.Mente 10.
Added to index2009-01-28
Total downloads36 ( #98,643 of 1,781,268 )
Recent downloads (6 months)8 ( #87,983 of 1,781,268 )
How can I increase my downloads?