Authors
Peter Spirtes
Carnegie Mellon University
Abstract
Whenever the use of non-experimental data for discovering causal relations or predicting the outcomes of experiments or interventions is contemplated, two difficulties are routinely faced. One is the problem of latent variables, or confounders: factors influencing two or more measured variables may not themselves have been measured or recorded. The other is the problem of sample selection bias: values of the variables or features under study may themselves influence whether a unit is included in the data sample.
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Identifying Intervention Variables.Michael Baumgartner & Isabelle Drouet - 2013 - European Journal for Philosophy of Science 3 (2):183-205.

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