MétaCan
Menu
Back to cohort
Record W2469842794 · doi:10.1097/ede.0000000000000521

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

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

Bibliographic record

VenueEpidemiology · 2016
Typeletter
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious Diseases
KeywordsOutcome (game theory)Counterfactual thinkingMediationContext (archaeology)Set (abstract data type)Consistency (knowledge bases)Representation (politics)Computer scienceDecompositionComponent (thermodynamics)PsychologySocial psychologyMathematicsChemistrySociologyPhysicsPolitical scienceArtificial intelligenceHistoryMathematical economics

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.348
GPT teacher head0.505
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueEpidemiologySame topicAdvanced Causal Inference TechniquesFrench-language works237,207