• Corpus ID: 220425355

Causal Effects in Twin Studies: the Role of Interference

@article{Smith2020CausalEI,
  title={Causal Effects in Twin Studies: the Role of Interference},
  author={Bonnie Kathryn Smith and Elizabeth L. Ogburn and Matt McGue and Saonli Basu and Daniel O. Scharfstein},
  journal={arXiv: Methodology},
  year={2020}
}
The use of twins designs to address causal questions is becoming increasingly popular. A standard assumption is that there is no interference between twins---that is, no twin's exposure has a causal impact on their co-twin's outcome. However, there may be settings in which this assumption would not hold, and this would (1) impact the causal interpretation of parameters obtained by commonly used existing methods; (2) change which effects are of greatest interest; and (3) impact the conditions… 

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References

SHOWING 1-10 OF 26 REFERENCES

Causal Interpretation of Between-Within Models for Twin Research

TLDR
It is shown that the BW model produces a specific subpopulation causal effect, in the absense of non-shared confounding, and that the causal interpretation can be retrieved with a minor modification of the model.

Beyond Heritability

TLDR
The heritability of human behavioral traits is now well established, due in large measure to classical twin studies, and environmental studies of discordant twin pairs and twin studies of genetic and environmental transactions are discussed.

Toward Causal Inference With Interference

TLDR
This article considers a population of groups of individuals where interference is possible between individuals within the same group, and proposes estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting.

Sibling effects on smoking in adolescence: evidence for social influence from a genetically informative design.

TLDR
This report extends prior research on sibling effects on smoking by identifying specific relationship dynamics that underlie transmission of risk within sibships and providing evidence that such relationship dynamics represent social rather than genetic processes.

Tests of the effects of adolescent early alcohol exposures on adult outcomes.

TLDR
Early alcohol exposures predict adult alcohol problems and related outcomes, despite stringent adjustment for measured and non-measured sources of potential confounding using propensity score and co-twin control.

Doubly robust estimation in observational studies with partial interference

TLDR
This paper proposes doubly robust (DR) estimators which utilize both models and are consistent and asymptotically normal if either model, but not necessarily both, is correctly specified.

Causal inference when counterfactuals depend on the proportion of all subjects exposed

TLDR
A complete interference setting is considered, in which each subject affects every other subject's outcome, and it is shown that a targeted maximum likelihood estimator for the i.i.d. setting can be used to estimate a class of causal parameters that includes direct effects and overall effects under certain interventions.

Dependent stressful life events and prior depressive episodes in the prediction of major depression: the problem of causal inference in psychiatric epidemiology.

TLDR
For environmental exposures in psychiatry that cannot be studied experimentally, co-twin control and propensity scoring methods--which have complementary strengths and weaknesses--can provide similar results, suggesting their joint use can help with the critical question of causal inference.

On causal inference in the presence of interference

TLDR
This article summarises some of the concepts and results from the existing literature and extends that literature in considering new results for finite sample inference, new inverse probability weighting estimators in the presence of interference and new causal estimands of interest.

Large Sample Randomization Inference of Causal Effects in the Presence of Interference

TLDR
This article considers inference about effects when the population consists of groups of individuals where interference is possible within groups but not between groups, and considers the effects of cholera vaccination and an intervention to encourage voting.