Hill’s Criterion ‘Experiment’: The Counterfactual Approach in Non-Radiation and Radiation Sciences

2021 ◽  
Vol 48 (12) ◽  
pp. 2149-2173
Author(s):  
A. N. Koterov ◽  
L. N. Ushenkova ◽  
A. P. Biryukov
2010 ◽  
Author(s):  
Jan Ketil Arnulf ◽  
John Erik Mathisen ◽  
Thorvald Haerem

PEDIATRICS ◽  
2006 ◽  
Vol 118 (6) ◽  
pp. e1721-e1733 ◽  
Author(s):  
Y. C. Wang ◽  
S. L. Gortmaker ◽  
A. M. Sobol ◽  
K. M. Kuntz

2019 ◽  
Vol 67 (5) ◽  
pp. 552-566
Author(s):  
Lucia Švábová ◽  
Marek Ďurica ◽  
Tomáš Klieštik

1980 ◽  
Vol 6 ◽  
pp. 119-138
Author(s):  
Richard Adler

The numerous difficulties facing the traditional Humean regularity approach to the problem of causation have been discussed in the literature at great length. In view of the current interest in possible worlds semantics, it is not surprising that the only serious alternative treatment of causation presently available, the counterfactual approach, has been explored recently as a means of circumventing the apparently unresolvable difficulties facing regularity causal theories. It is the purpose of this paper to suggest that such a strategy holds little promise. Specifically, I will argue that, in addition to giving rise to problems directly analogous to those facing regularity accounts, the counterfactual approach fails in principle to reflect important properties of causal relations as we understand them intuitively. David Lewis's possible worlds account, the most comprehensive counterfactual theory to date, is further criticized for implicit problems with natural lawhood even more serious than those typically raised for regularity accounts, for additional inadequacies in its analysis of causal relations, and for its failure to satisfy basic empiricist epistemological standards.


2019 ◽  
Vol 28 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Sam Baron ◽  
Mark Colyvan ◽  
David Ripley

ABSTRACT Our goal in this paper is to extend counterfactual accounts of scientific explanation to mathematics. Our focus, in particular, is on intra-mathematical explanations: explanations of one mathematical fact in terms of another. We offer a basic counterfactual theory of intra-mathematical explanations, before modelling the explanatory structure of a test case using counterfactual machinery. We finish by considering the application of counterpossibles to mathematical explanation, and explore a second test case along these lines.


Agribusiness ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 167-191 ◽  
Author(s):  
Benedetto Rocchi ◽  
Donato Romano ◽  
Ahmad Sadiddin ◽  
Gianluca Stefani

Author(s):  
Silvia Chiappa

We consider the problem of learning fair decision systems from data in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a counterfactual approach to disregard effects along unfair pathways that does not incur in the same loss of individual-specific information as previous approaches. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. We leverage recent developments in deep learning and approximate inference to develop a VAE-type method that is widely applicable to complex nonlinear models.


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