Corpus ID: 210921278

Identification of Causal Diffusion Effects Under Structural Stationarity.

@article{Egami2020IdentificationOC,
  title={Identification of Causal Diffusion Effects Under Structural Stationarity.},
  author={Naoki Egami},
  journal={arXiv: Methodology},
  year={2020}
}
  • Naoki Egami
  • Published 2020
  • Computer Science, Mathematics
  • arXiv: Methodology
Although social and biomedical scientists have long been interested in the process through which ideas and behaviors diffuse, the identification of causal diffusion effects, also known as peer and contagion effects, remains challenging. Many scholars consider the commonly used assumption of no omitted confounders to be untenable due to contextual confounding and homophily bias. To address this long-standing problem, we examine the causal identification under a new assumption of structural… Expand
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References

SHOWING 1-10 OF 90 REFERENCES
Estimating Peer Effects in Longitudinal Dyadic Data Using Instrumental Variables
TLDR
This paper investigates how causal peer effects of traits and behaviors can be identified using genes (or other structurally isomorphic variables) as instrumental variables (IV) in a large set of data generating models with homophily and confounding and shows that IV identification of peer effects remains possible even under multiple complications often regarded as lethal. Expand
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining theExpand
Statistical Tests for Contagion in Observational Social Network Studies
TLDR
A general method to lower bound the strength of causal effects in observational social network studies, even in the presence of arbitrary, unobserved individual traits is demonstrated. Expand
Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects
TLDR
It is shown that high-dimensional adjustment of a nonexperimental control group using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment, which demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation. Expand
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
TLDR
A dynamic matched sample estimation framework is developed to distinguish influence and homophily effects in dynamic networks, and this framework is applied to a global instant messaging network of 27.4 million users, finding that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and thathomophily explains >50% of the perceived behavioral contagion. Expand
Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
TLDR
The authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation can produce substantial correlations between an individual’s enduring traits and his or her choices, even when there is no intrinsic affinity between them. Expand
Social Networks and the Identification of Peer Effects
There is a large and growing literature on peer effects in economics. In the current article, we focus on a Manski-type linear-in-means model that has proved to be popular in empirical work. WeExpand
Instrumental variables estimates of peer effects in social networks.
TLDR
This paper shows that instrumental variables (IVs) can help to address problems in peer effects in order to provide causal estimates of peer effects, and finds consistent evidence for peer effects on smoking. Expand
Identification of causal intervention effects under contagion
TLDR
This paper proposes causal intervention effects in two- person partnerships under arbitrary infectious disease transmission dynamics, and gives nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. Expand
Social Networks and Causal Inference
This chapter reviews theoretical developments and empirical studies related to causal inference on social networks from both experimental and observational studies. Discussion is given to the effectExpand
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