Elements of estimation theory for causal effects in the presence of network interference

By Daniel Lewis Sussman, Edoardo M. Airoldi. 2017

78 citations recorded by the source corpus.

Randomized experiments in which the treatment of a unit can affect the outcomes of other units are becoming increasingly common in healthcare, economics, and in the social and information sciences. From a causal inference perspective, the typical assumption of no interference becomes untenable in such experiments. In many problems, however, the patterns of interference may be informed by the observation of network connections among the units of analysis. Here, we develop elements of optimal estimation theory for causal effects leveraging an observed network, by assuming that the potential outcomes of an individual depend only on the individual's treatment and on the treatment of the neighbors. We propose a collection of exclusion restrictions on the potential outcomes, and show how subsets of these restrictions lead to various parameterizations. Considering the class of linear unbiased estimators of the average direct treatment effect, we derive conditions on the design that lead to the existence of unbiased estimators, and offer analytical insights on the weights that lead to minimum integrated variance estimators. We illustrate the improved performance of these estimators when compared to more standard biased and unbiased estimators, using simulations.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/40223ec272541aa4047da7ba420e7836a024a391

How researchers see this paper

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

In Computer Networks and Communications

In Statistical and Nonlinear Physics

In Statistics and Probability

Citations in the corpus

Causal Inference for a Single Group of Causally-Connected Units Under Stratified Interference (2017, methodology): “Methodology using this approach has been proposed by Aronow and Samii (2012); van der Laan (2014); Sofrygin and van der Laan (2015); Forastiere et al. (2016); Jagadeesan et al. (2017); Ogburn et al. (2017); Sussman and Airoldi (2017); Tchetgen Tchetgen et al. (2017).”
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference (2020, methodology): “…interference, Aronow et al. (2017) provide a general framework to translate diﬀerent assumptions about interference into inverse-probability estimators, and Sussman and Airoldi (2017) give linearly unbiased, minimum integrated-variance estimators under a series of assumptions about interference.”