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The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Four Types of Effect Modification: A Classification Based on Directed Acyclic Graphs

VanderWeele, Tyler J., Robins, James M.·2007·Epidemiology

Abstract

It is possible to classify the types of causal relationships that can give rise to effect modification on the risk difference scale by expressing the conditional causal risk-difference as a sum of products of stratum-specific risk differences and conditional probabilities. Directed acyclic graphs clarify the causal relationships necessary for a particular variable to serve as an effect modifier for the causal risk difference involving 2 other variables. The directed acyclic graph causal framework thereby gives rise to a 4-fold classification for effect modification: direct effect modification, indirect effect modification, effect modification by proxy and effect modification by a common cause. We briefly discuss the case of multiple effect modification relationships and multiple effect modifiers as well as measures of effect other than that of the causal risk difference.

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Show all 11 references

Research works printed but not linked: 12

  1. Greenland, 2002, An overview of relations among causal modelling methods., Int J Epidemiol, 31, 1030, 10.1093/ije/31.5.1030 · DOI · Paper page
  2. Greenland, 1988, Invariants and noninvariants in the concept of interdependent effects., Scand J Work Environ Health, 14, 125, 10.5271/sjweh.1945 · DOI
  3. Greenland, 1980, Control of confounding in the assessment of medical technology., Int J Epidemiol, 9, 361, 10.1093/ije/9.4.361 · DOI · Paper page
  4. Rosenbaum, 1984, The consequences of adjustment for a concomitant variable that has been affected by the treatment., J Roy Stat Soc Ser A, 147, 656, 10.2307/2981697 · DOI · Paper page
  5. Robins, 1987, The foundations of confounding in epidemiology., Math Modeling, 14, 869
  6. Robins, 1989, The control of confounding by intermediate variables., Stat Med, 8, 679, 10.1002/sim.4780080608 · DOI · Paper page
  7. Verma, 1988, Causal networks: Semantics and expressiveness. In: Shachter R, Levitt TS, Kanal LN. Proceedings of the 4th Workshop on Uncertainty in Artificial Intelligence, 352-359. Reprinted in Uncertainty in Artificial Intelligence., Amsterdam: Elsevier, 4, 69
  8. Geiger, 1990, Identifying independence in bayesian networks., Networks, 20, 507, 10.1002/net.3230200504 · DOI · Paper page
  9. Lauritzen, 1990, Independence properties of directed markov fields., Networks, 20, 491, 10.1002/net.3230200503 · DOI · Paper page
  10. Rosenbaum, 1983, The central role of the propensity score in observational studies for causal effects., Biometrika, 70, 41, 10.1093/biomet/70.1.41 · DOI · Paper page
  11. Robins, 1987, Addendum to a new approach to causal inference in mortality studies with sustained exposure period - application to control of the healthy worker survivor effect., Computers Mathematics Applications, 14, 923, 10.1016/0898-1221(87)90238-0 · DOI · Paper page
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Metadata

Authors
VanderWeele, T. J., & Robins, J. M.
Venue
Epidemiology
Published
2007-09
Volume
18
Issue
5
Pages
561-568
ISSN
1044-3983 (ISSN-L)
OpenAlex
W2075200608
Cited by
205
References
11
FWCI
6.61
Type
journal article
Language
en
Open access
closed

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VanderWeele, T. J., & Robins, J. M. (2007). Four Types of Effect Modification: A Classification Based on Directed Acyclic Graphs. Epidemiology. https://doi.org/10.1097/ede.0b013e318127181b

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