The Sufficient Cause Framework in Statistics, Philosophy and the Biomedical and Social Sciences

VanderWeele, Tyler J.

doi:10.1002/9781119945710.ch13

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…Pearl and Halpern (2005) have argued that situations which involve a choice between two alternative singular/actual/token causes, each of which might be sufficient for the effect, may be resolved by placing the basic causal model within a more extended network. Their solution has been contested by VanderWeele (2009VanderWeele ( , 2012, and others, but the debate clearly demonstrates the symbiotic relationship between large and small N causal networks. The famous singular case, which has animated endless philosophical debate (Hall, 2004) concerns two projectiles, both of which could have smashed a glass target.…”

Section: The Interplay Of Large N Causal Network and Narrativesmentioning

confidence: 99%

Ethnographic Causality

Abell¹,

Engel²

2023

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Section: The Interplay Of Large N Causal Network and Narrativesmentioning

confidence: 99%

Ethnographic Causality

Abell¹,

Engel²

2023

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“…One representative example of "the probabilities of the potential outcome types" is "the probabilities of causation", which probabilistically evaluate the "necessity cause", "sufficiency cause", and "necessity and sufficiency cause" (Cai and Kuroki 2005;Dawid, Musio, and Fienberg 2016;Dawid, Musio, and Murtas 2017;Dawid and Musio 2022;Pearl 2015;VanderWeele 2012). Pearl (2009) and Tian and Pearl (2000) developed formal semantics for the probabilities of causation based on structural causal models.…”

Section: Theoretical Background and Contributionmentioning

confidence: 99%

Probabilities of Potential Outcome Types in Experimental Studies: Identification and Estimation Based on Proxy Covariate Information

Shingaki

Kuroki

2023

AAAI

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The concept of potential outcome types is one of the fundamental components of causal inference. However, even in randomized experiments, assumptions on the data generating process, such as monotonicity, are required to evaluate the probabilities of the potential outcome types. To solve the problem without such assumptions in experimental studies, a novel identification condition based on proxy covariate information is proposed in this paper. In addition, the estimation problem of the probabilities of the potential outcome types reduces to that of singular models when they are identifiable through the proposed condition. Thus, they cannot be evaluated by standard statistical estimation methods. To overcome this difficulty, new plug-in estimators of these probabilities are presented, and the asymptotic normality of the proposed estimators is shown.

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“…Numerous applications of abstract algebra in sufficient causes were demonstrated in [1] and [34]. Statistical methods were used in sufficient causes long ago [2], [15], [20], [32], [33], and this trend continues to grow [29], [31], [35], [37]. The position of the sufficient-component cause model among other causal modelling methods presented in [9].…”

Section: Introductionmentioning

confidence: 99%

Boolean Algebras and Classification of Interactions in Sufficient-Component Cause Model

Panov¹,

Nagrebetskaya²

2015

Int. J. of Pure and Appl. Math.

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A mathematical model of the sufficient-component cause framework is considered based on the theories of Boolean algebra. The model consists of the space of states of a binary experiment and a set of symmetries of the experiment. The space of states is a Boolean algebra of n Boolean variables where n is the number of the binary causes in the experiment. The set of symmetries of the experiment is a subgroup of the group of all automorphisms of Boolean algebra of the states of experiment. This subgroup is generated by transformations preserving a type of interaction. An experimenter should deduce these transformations from the peculiar properties of the experiment. Examples of such transformations are provided. Classification of interactions is obtained by the calculation of the orbits of action of the group of symmetries on the space of states of the experiment. It is shown that the classification of the interaction for the ordinary symmetries of sufficient causes is the same as reported in related works. Other symmetries of the binary experiment are considered as well. It is shown that the corresponding classification of the interaction types in a binary experiment depends substantially on the symmetries of the experiment. Statistical criteria of particular types of responses are proven and the problem

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The Sufficient Cause Framework in Statistics, Philosophy and the Biomedical and Social Sciences

Cited by 4 publications

References 43 publications

Ethnographic Causality

Ethnographic Causality

Probabilities of Potential Outcome Types in Experimental Studies: Identification and Estimation Based on Proxy Covariate Information

Boolean Algebras and Classification of Interactions in Sufficient-Component Cause Model

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