David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Synthese 163 (3):433 - 442 (2008)
We consider the problems arising from using sequences of experiments to discover the causal structure among a set of variables, none of whom are known ahead of time to be an “outcome”. In particular, we present various approaches to resolve conflicts in the experimental results arising from sampling variability in the experiments. We provide a sufficient condition that allows for pooling of data from experiments with different joint distributions over the variables. Satisfaction of the condition allows for an independence test with greater sample size that may resolve some of the conflicts in the experimental results. The pooling condition has its own problems, but should—due to its generality—be informative to techniques for meta-analysis.
|Keywords||Causal discovery Search Aggregating independence tests Experimental design|
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References found in this work BETA
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Judea Pearl (2009). Causality. Cambridge University Press.
Peter Spirtes, Clark Glymour & Richard Scheines (1996). Causation, Prediction, and Search. British Journal for the Philosophy of Science 47 (1):113-123.
Frederick Eberhardt, Clark Glymour & Richard Scheines, N-1 Experiments Suffice to Determine the Causal Relations Among N Variables.
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