Contrastive statistical explanation and causal heterogeneity

Abstract
Probabilistic phenomena are often perceived as being problematic targets for contrastive explanation. It is usually thought that the possibility of contrastive explanation hinges on whether or not the probabilistic behaviour is irreducibly indeterministic, and that the possible remaining contrastive explananda are token event probabilities or complete probability distributions over such token outcomes. This paper uses the invariance-under-interventions account of contrastive explanation to argue against both ideas. First, the problem of contrastive explanation also arises in cases in which the probabilistic behaviour of the explanandum is due to unobserved causal heterogeneity. Second, it turns out that, in contrast to the case of pure indeterminism, the plausible contrastive explananda under causal heterogeneity are not token event probabilities, but population-level statistical facts
Keywords Contrastive explanation  Statistics  Heterogeneity  Indeterminism  Invariance
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DOI 10.1007/s13194-012-0050-1
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References found in this work BETA
Causality: Models, Reasoning, and Inference.Judea Pearl - 2000 - Cambridge University Press.
Philosophical Papers Vol. II.Lewis David - 1986 - Oxford University Press.

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Causal Comparability, Causal Generalizations, and Epistemic Homogeneity.Rosa W. Runhardt - 2017 - Philosophy of the Social Sciences 47 (3):183-208.

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