Causal Discovery and the Problem of Ignorance. An Adaptive Logic Approach

Journal of Applied Logic 7 (2):188-205 (2009)
In this paper, I want to substantiate three related claims regarding causal discovery from non-experimental data. Firstly, in scientific practice, the problem of ignorance is ubiquitous, persistent, and far-reaching. Intuitively, the problem of ignorance bears upon the following situation. A set of random variables V is studied but only partly tested for (conditional) independencies; i.e. for some variables A and B it is not known whether they are (conditionally) independent. Secondly, Judea Pearl’s most meritorious and influential algorithm for causal discovery (the IC algorithm) cannot be applied in cases of ignorance. It presupposes that a full list of (conditional) independence relations is on hand and it would lead to unsatisfactory results when applied to partial lists. Finally, the problem of ignorance is successfully treated by means of ALIC, the adaptive logic for causal discovery presented in this paper.
Keywords adaptive logic  causal discovery  causal Bayes nets
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DOI 10.1016/j.jal.2007.11.004
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Austin Bradford Hill (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine 58:295-300.

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