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  1. A formal framework for representing mechanisms?Alexander Gebharter - 2014 - Philosophy of Science 81 (1):138-153.
    In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the (...)
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  • A general identification condition for causal effects.Judea Pearl - manuscript
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  • Causal inference in the presence of latent variables and selection bias.Peter Spirtes, Christopher Meek & Thomas Richardson - unknown
    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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