Designs for estimating the treatment effect in networks with interference

Jagadeesan, Ravi; Pillai, Natesh S.; Volfovsky, Alexander

doi:10.1214/18-aos1807

Cited by 15 publications

(19 citation statements)

References 21 publications

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“…New methods for learning about causal effects from social network data rely on assumptions that are consistent with DAG models, explicitly in the case of methods for observational data proposed by van der Laan (2014) and and implicitly in many of the methods that are based on randomized experiments (e.g. Aronow and Samii (2017), Athey et al (2018), Bowers et al (2013), Choi (2014), Eckles et al (2016), Forastiere et al (2020), Graham et al (2010), Raudenbush (2006, 2008), Hudgens and Halloran (2008), Jagadeesan et al (2020), Liu and Hudgens (2014), Liu et al (2016), Rosenbaum (2007), Rubin (1990), Sobel (2006), Tchetgen Tchetgen and VanderWeele (2012) and VanderWeele (2010)). However, as we shall show in the next section and as has been acknowledged by some of the aforementioned researchers, DAGs in these settings can run into difficulties in practice.…”

Section: Graphical Models For Social Interactionsmentioning

confidence: 99%

“…(), Hong and Raudenbush (), Hudgens and Halloran (), Jagadeesan et al . (), Liu and Hudgens (), Liu et al . (), Rosenbaum (), Rubin (), Sobel (), Tchetgen Tchetgen and VanderWeele () and VanderWeele ()).…”

Section: Graphical Models For Social Interactionsunclassified

“…However, recently there has been increasing interest in settings where treatments 'spill over' from the treated individual to his or her social contacts, or where outcomes are contagious. Researchers who are interested in causal inference have developed methods for interferencewhen one individual's treatment or exposure affects not only his own outcome but also the outcomes of his contacts (Aronow and Samii, 2017;Athey et al, 2018;Bowers et al, 2013;Eckles et al, 2016;Forastiere et al, 2020;Graham et al, 2010;Halloran and Hudgens, 2012;Raudenbush, 2006, 2008;Hudgens and Halloran, 2008;Jagadeesan et al, 2020;Liu and Hudgens, 2014;Liu et al, 2016;Ogburn and VanderWeele, 2014;Rosenbaum, 2007;Rubin, 1990;Sobel, 2006;Tchetgen Tchetgen and VanderWeele, 2012;VanderWeele, 2010). Researchers who are interested in social networks have attempted to model the spread of contagious outcomes across network ties (Christakis and Fowler, 2007, 2008, 2010Ali and Dwyer, 2009;Cacioppo et al, 2009;Lazer et al, 2010;Rosenquist et al, 2010), but existing methods for such modelling are either flawed (Cohen-Cole and Fletcher, 2008;Lyons, 2011;Shalizi and Thomas, 2011) or limited by strong assumptions and burdensome data requirements.…”

Section: Introductionmentioning

confidence: 99%

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Causal Inference, Social Networks and Chain Graphs

Ogburn

Shpitser

Lee

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is infeasible in practice to collect in most settings and, second, the models are high dimensional and often too big to fit to the available data. We illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data-generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks by using data from US Supreme Court decisions between 1994 and 2004 and in simulations.

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Section: Graphical Models For Social Interactionsmentioning

confidence: 99%

Section: Graphical Models For Social Interactionsunclassified

Section: Introductionmentioning

confidence: 99%

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Causal Inference, Social Networks and Chain Graphs

Ogburn

Shpitser

Lee

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Methods such as Jagadeesan et al . () and Athey et al . () that use restricted randomizations to improve statistical power and computational speed might also be considered.…”

Section: Discussionmentioning

confidence: 87%

“…The proposed method was further evaluated using a different (symmetric) interference structure that was generated as a linear preferential attachment network following Jagadeesan et al . (). Settings where the uniformity failure times were correlated between individuals and were correlated with censoring times were also considered.…”

Section: Right‐censored Failure Time Outcomesmentioning

confidence: 97%

Randomization inference with general interference and censoring

et al. 2019

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Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is “partial interference,” in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) consider randomization‐based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right‐censored outcomes is challenging because standard randomization‐based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676–692) for two‐sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh.

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Network and covariate adjusted response‐adaptive design for binary response

Mei,

Xie,

Qin

et al. 2023

Statistics in Medicine

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Randomization is a distinguishing feature of clinical trials for unbiased assessment of treatment efficacy. With a growing demand for more flexible and efficient randomization schemes and motivated by the idea of adaptive design, in this article we propose the network and covariate adjusted response‐adaptive (NCARA) design that can concurrently manage three challenges: (1) maximizing benefits of a trial by assigning more patients to the superior treatment group randomly; (2) balancing social network ties across treatment arms to eliminate potential network interference; and (3) ensuring balance of important covariates, such as age, gender, and other potential confounders. We conduct simulation with different network structures and a variety of parameter settings. It is observed that the NCARA design outperforms four alternative randomization designs in solving the above‐mentioned problems and has comparable power and type I error for detecting true difference between treatment groups. In addition, we conduct real data analysis to implement the new design in two clinical trials. Compared to equal randomization (the original design utilized in the trials), the NCARA design slightly increases power, largely increases the percentage of patients assigned to the better‐performing group, and significantly improves network and covariate balances. It is also noted that the advantages of the NCARA design are augmented when the sample size is small and the level of network interference is high. In summary, the proposed NCARA design assists researchers in conducting clinical trials with high‐quality and high‐efficiency.

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Designs for estimating the treatment effect in networks with interference

Cited by 15 publications

References 21 publications

Causal Inference, Social Networks and Chain Graphs

Causal Inference, Social Networks and Chain Graphs

Randomization inference with general interference and censoring

Network and covariate adjusted response‐adaptive design for binary response

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