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Cited by 106Advanced Causal Inference TechniquesBayesian Modeling and Causal InferenceStatistical Methods and Bayesian Inference

Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding

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Shpitser, Ilya·2013·Cognitive Science
DOI 10.1111/cogs.12058
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Abstract

Questions concerning mediated causal effects are of great interest in psychology, cognitive science, medicine, social science, public health, and many other disciplines. For instance, about 60% of recent papers published in leading journals in social psychology contain at least one mediation test (Rucker, Preacher, Tormala, & Petty, 2011). Standard parametric approaches to mediation analysis employ regression models, and either the “difference method” (Judd & Kenny, 1981), more common in epidemiology, or the “product method” (Baron & Kenny, 1986), more common in the social sciences. In this article, we first discuss a known, but perhaps often unappreciated, fact that these parametric approaches are a special case of a general counterfactual framework for reasoning about causality first described by Neyman (1923) and Rubin (1924) and linked to causal graphical models by Robins (1986) and Pearl (2006). We then show a number of advantages of this framework. First, it makes the strong assumptions underlying mediation analysis explicit. Second, it avoids a number of problems present in the product and difference methods, such as biased estimates of effects in certain cases. Finally, we show the generality of this framework by proving a novel result which allows mediation analysis to be applied to longitudinal settings with unobserved confounders.

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  • Shpitser provides complete algorithms for identifying natural direct and indirect effects and extends these results to path-specific effects with multiple treatments and multiple outcomes.

    “Shpitser [24] further provides complete algorithms for identifying natural direct and indirect effects and extends these results to path-specific effects with multiple treatments and multiple outcomes.”

    From The Deductive Approach to Causal Inference (2014) · cites this paper as [24]

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Research works printed but not linked: 9

  1. Huang, Y., & Valtorta, M. (2006). Pearl's calculus of interventions is complete. In Proceedings of the twenty-second annual conference on uncertainty in artificial intelligence (UAI-06) (pp. 217-224).
  2. Pearl, J. (2001). Direct and indirect effects. In Proceedings of the seventeenth conference annual conference on uncertainty in artificial intelligence (UAI-01) (pp. 411-420). San Francisco: Morgan Kaufmann.
  3. Richardson, T. S. (2009). A factorization criterion for acyclic directed mixed graphs. In Proceedings of the twenty-fifth annual conference on uncertainty in artificial intelligence (UAI-09) (pp. 463-470). Corvallis, OR: AUAI Press.
  4. Richardson, T. S., Robins, J. M., & Shpitser, I. (2012). Nested markov properties for acyclic directed mixed graphs. In Proceedings of the twenty-fifth conference annual conference on uncertainty in artificial intelligence (UAI-12) (pp. 13-13). Corvallis, OR: AUAI Press.
  5. Robins, J. M., & Wasserman, L. (1997). Estimation of effects of sequential treatments by reparameterizing directed acyclic graphs. In Proceedings of the 13th annual conference on uncertainty in artificial intelligence (UAI-97) (pp. 409-420). San Francisco: Morgan Kaufmann.
  6. Shpitser, I., & Pearl, J. (2006a). Identification of conditional interventional distributions. In R. Dechter and T.S. Richardson (Eds.), Proceedings of the twenty-second conference on uncertainty in artificial intelligence (UAI-06) (pp. 437-444). Corvallis, OR: AUAI Press.
  7. Shpitser, I., & Pearl, J. (2006b). Identification of joint interventional distributions in recursive semi- markovian causal models. In Proceedings of the 21st national conference on artificial intelligence (AAAI 06) (pp. 1219-1226). Palo Alto, CA: AAAI Press.
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  9. Woodworth, R. S. (1928). Dynamic psychology. In C. Murchison (Ed.), Psychologies of 1925. Worcester, MA: Clark University Press.

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Authors
Shpitser, I.
Affiliations
University of Southampton (GB)
Corresponding author
Shpitser, Ilya
ORCID
Shpitser, Ilya
Venue
Cognitive Science
Publisher
Wiley
Published
2013-07-30
Volume
37
Issue
6
Pages
1011-1035
ISSN
  • 0364-0213 (ISSN-L)
  • 1551-6709
DOI
10.1111/cogs.12058
OpenAlex
W2152431454
Cited by
106
References
33
Citations by year
2026: 3 · 2025: 7 · 2024: 2 · 2023: 8 · 2022: 12 · 2021: 10 · 2020: 19 · 2019: 13 · 2018: 10 · 2017: 8 · 2016: 3 · 2015: 4 · 2014: 6
FWCI
3.81
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Cognitive Science (publishedVersion)
  • PubMed (publishedVersion)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
unmeasured confounding, product method, difference method, causal inference, causal graphical models, counterfactual reasoning
MeSH terms
Humans, Models, Theoretical, Models, Statistical, Causality

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