David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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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
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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.
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