On causal inference in the presence of interference

By E. T. Tchetgen Tchetgen, Tyler J. VanderWeele. Statistical Methods in Medical Research, 2010

505 citations recorded by the source corpus.

Interference is said to be present when the exposure or treatment received by one individual may affect the outcomes of other individuals. Such interference can arise in settings in which the outcomes of the various individuals come about through social interactions. When interference is present, causal inference is rendered considerably more complex, and the literature on causal inference in the presence of interference has just recently begun to develop. In this article we summarise some of the concepts and results from the existing literature and extend 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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6a10be9c1cca3beb7a5a7b56132b3aab96a67239

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Estimating Average Causal Effects Under General Interference (2012, background): “Tchetgen-Tchetgen and VanderWeele (2010) extend Hudgens and Halloran’s results, providing conservative variance estimators, a framework for finite sample inference, and extensions to observational studies.”
Causal diagrams for interference (2014, methodology): “Sobel (2006), Hudgens and Halloran (2008), VanderWeele and Tchetgen Tchetgen (2011b) and Tchetgen Tchetgen and VanderWeele (2012) proposed ways to estimate and extend unit-level, spillover, total and overall effects.”
Large sample randomization inference of causal effects in the presence of interference (2014, methodology): “Rather than relying on large sample approximations, Tchetgen Tchetgen and VanderWeele (2012) derived exact CIs for various casual effects in the setting where the outcome is binary based on the Hoeffding inequality.”
Semiparametric theory and empirical processes in causal inference (2015, background): “rence in case control studies [58, 69, 50, 46, 70] and matched cohort studies [63, 20]. There has also been progress made for causal inference in studies using network data with possible interference [19, 52, 30, 61]. Much more work is needed in settings related to both study designs with non-standa”
All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework. (2015, background): “In addition, we assumed that each participant’s potential outcomes did not depend on the exposures of other participants, meaning that we did not explore violations of the assumption of no interference.(18,19) Finally, in the example presented here, we had positivity with respect to U.”
Identification and Estimation of Spillover Effects in Randomized Experiments (2017, background): “Tchetgen Tchetgen and VanderWeele (2012) and Rigdon and Hudgens (2015) propose finite sample confidence intervals, while Liu and Hudgens (2014) study confidence intervals in a large-sample randomization inference context.”
A Survey on Causal Inference (2020, background): “Extensive work has been developed on identification and estimation of causal parameters under interference [54, 88, 95, 136].”
Graph cluster randomization: network exposure to multiple universes (2013, background): “Our work thus occupies a mediating perch between recent work from the statistical literature on causal inference under interference [1, 2, 15], as well as recent work from the computer science literature on network bucket testing [3, 11].”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “Finally, we did not consider target trials with interference (52 – 54) or crossover, and we ignored effects that may result from the scaling up of the treatment strategies outside of the studied populations.”
Random graph asymptotics for treatment effect estimation under network interference (2020, background): “There is some existing literature on this task; however, it has mostly focused on a setting where one has access to many independent networks (Baird et al., 2018; Basse and Feller, 2018; Hudgens and Halloran, 2008; Tchetgen Tchetgen and VanderWeele, 2012).”
Causal Inference for a Single Group of Causally-Connected Units Under Stratified Interference (2017, methodology): “Like other methods proposed for estimating spillover effects (Hudgens and Halloran, 2008; Tchetgen Tchetgen and VanderWeele, 2012), ours will be most appropriate when the number of groups is moderate to large.”