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Enregistrement W2469842794 · doi:10.1097/ede.0000000000000521

Sufficient Cause Representation of the Four-way Decomposition for Mediation and Interaction

2016· letter· en· W2469842794 sur OpenAlexaffabout
Tyler J. VanderWeele, Ian Shrier

Notice bibliographique

RevueEpidemiology · 2016
Typeletter
Langueen
DomaineMathematics
ThématiqueAdvanced Causal Inference Techniques
Établissements canadiensMcGill UniversityJewish General Hospital
Organismes subventionnairesNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious Diseases
Mots-clésOutcome (game theory)Counterfactual thinkingMediationContext (archaeology)Set (abstract data type)Consistency (knowledge bases)Representation (politics)Computer scienceDecompositionComponent (thermodynamics)PsychologySocial psychologyMathematicsChemistrySociologyPhysicsPolitical scienceArtificial intelligenceHistoryMathematical economics

Résumé

récupéré en direct d'OpenAlex

To the Editor: Recent work1 has shown how a total effect of an exposure on an outcome, in the context of a mediator with which the exposure might interact, could be decomposed into four components: that due to just mediation, that due to just interaction, that due to both mediation and interaction, and that due to neither mediation nor interaction. In this research letter, we show how each of these four components can be expressed within the sufficient cause framework allowing for mediation.2,3 Let A be an exposure, M a mediator, and Y an outcome. For simplicity, we consider the case in which A, M, and Y are all binary. Within the counterfactual framework, we define Ya as the outcome Y we would have observed if A had been set to a. The total effect is defined by Y1 – Y0. We define Yam as the potential outcome Y if A were set to a, and M were set to m. We define Ma as the potential outcome M if A were set to a. We make consistency assumptions that Ya = Y and Ma = M when A = a, and that Yam = Y when A = a and M = m; we also make the composition assumption4 that Ya = YaMa. The four-way decomposition of the total effect can be written as where the four components are as follows: the controlled direct effect (CDE) is given by (Y10 – Y00) and is the component due to neither mediation nor interaction; the reference interaction (INTref) is given by (Y11 – Y10 – Y01 + Y00)M0 and is the component due to interaction but not mediation; the mediated interaction (INTmed) is given by (Y11 – Y10 – Y01 + Y00) (M1 – M0) and is the component due to both mediation and interaction; and the pure indirect effect (PIE) is given by (Y01 – Y00) (M1 – M0) and is the component due to just mediation, not interaction. The first two components, the controlled direct effect and reference interaction, sum to the direct effect that is generally used in the mediation literature (also referred to as the “pure direct effect”5 or one type of “natural direct effect”6); and the third and fourth components, the mediated interaction and the pure indirect effect sum to the indirect effect that is generally used in the mediation literature (also referred to as the “total indirect effect”5 or one type of “natural indirect effect”6) Further discussion of the interpretation of these components, the assumptions needed to estimate them from data on average for a population, and statistical methods to do so are described in further detail in VanderWeele.1 Here, we will relate these four components to the sufficient cause framework allowing for mediation.2,3,7,8 A sufficient cause model for a particular outcome posits a collection of different mechanisms each of which is capable of bringing about the outcome under consideration. A particular mechanism operates when some minimal set of actions, events, or states of nature is obtained; when all components required for the mechanism are present, the outcome inevitably occurs. These mechanisms are thus referred to as “sufficient causes” since the conjunction of all the components required for a particular mechanism to operate is sufficient for that outcome; the individual components required for particular mechanisms are then each referred to as “component causes.” Hafeman2 and VanderWeele3 considered the representation of mediation within this sufficient cause framework. Both considered a situation in which the exposure A never prevents the intermediate M or the outcome Y and in which the intermediate M never prevents the outcome Y. Such assumptions are sometimes referred to as monotonicity assumptions and they may or may not be reasonable assumptions in any given context. Under such monotonicity assumptions, there are two possible sufficient causes for the M: one sufficient cause that requires A and possibly some other factors, denoted here by J, to operate; and a second sufficient cause that may operate irrespective of whether A is present, provided some other factors, denoted by K, are present. In the context of exposure A and mediator M, for the outcome Y there are 4 sufficient causes: one involving both A and M and possibly some other factors F; one involving just A and possibly some other factors C; one involving just M and possibly some other factors B; and one requiring neither A nor M but simply some other factors L. The two sufficient causes for M are thus K and AJ; the four sufficient causes for Y are L, BM, CA, and FAM. Hafeman2 and VanderWeele3 discussed the graphical representation8 of these sufficient causes which we give in Figure 1 and also the relation of direct and indirect effects to the background components of the sufficient cause model, namely K, J, L, B, C, and F.FIGURE: Sufficient causes for mediator M and outcome Y depicting mediation.Here, we provide similar relations for the components of the 4-way decomposition. In the Appendix, we show that we can express the average value for a population of each of the four components of the four-way decomposition as Several interesting insights emerge from these expressions. For the CDE to be present for an individual, the sufficient cause for Y involving A must be present (C = 1) and that involving neither A nor M must be absent (L = 0). For the reference interaction to be present for an individual, the sufficient cause for M that does not require A must be present (K = 1) and then the magnitude of the reference interaction is further determined by the portion of AM causing Y that occurs because K = 1. This is the difference between (1) the likelihood of the interactive sufficient cause for Y requiring both A and M being present (F = 1) with all other sufficient causes being absent (B = 0, C = 0, L = 0), and (2) the likelihood of all of the sufficient causes for Y being present except that requiring neither A nor M (i.e., F = 1, B = 1, C = 1, L = 0). Note that in cases of “competing antagonism”9 in which the outcome Y occurs if either A or M or both are present (in Figure 1 if F = 1, B = 1, C = 1), this decreases the magnitude of the reference interaction. This is because, in those cases, the effect of both A and M together is the same as the effect of just A or of just M alone, and thus the effect of both together is smaller than the sum of just A and of just M. For the mediated interaction to be present for an individual, the sufficient cause for M that does not require A must be absent (K = 0) and the one requiring A must be present (J = 1). Then, the magnitude of the mediated interaction is further determined by the difference between (1) the likelihood of the interactive sufficient cause for Y requiring both A and M being present (F = 1) with all other sufficient causes being absent (B = 0, C = 0, L = 0), and (2) the likelihood of all of the sufficient causes for Y being present except that requiring neither A nor M (i.e., F = 1, B = 1, C = 1, L = 0). For the pure indirect effect to be present for an individual, the sufficient cause for M that does not require A must be absent (K = 0) and the one requiring A must be present (J = 1). Then, it also must be the case that the sufficient cause for Y that involves M must be present (B = 1), but the one requiring neither A nor M must be absent (K = 0). These expressions describe the four components of the four-way mediation–interaction decomposition in terms of sufficient causes. Tyler J. VanderWeele Department of Epidemiology Harvard School of Public Health Boston, MA [email protected] Ian Shrier Centre for Clinical Epidemiology Lady Davis Institute for Medical Research Jewish General Hospital McGill University Montreal, QC, Canada

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,065
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,085

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,065
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,004
Communication savante0,0030,005
Science ouverte0,0040,002
Intégrité de la recherche0,0060,010
Charge utile insuffisante (le modèle a refusé de juger)0,0210,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,348
Tête enseignante GPT0,505
Écart entre enseignants0,157 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2016
Routes d'admission2
Résumé présentoui

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