AVERAGE TREATMENT EFFECTS IN THE PRESENCE OF UNKNOWN INTERFERENCE.

@article{Svje2021AVERAGETE,
  title={AVERAGE TREATMENT EFFECTS IN THE PRESENCE OF UNKNOWN INTERFERENCE.},
  author={Fredrik S{\"a}vje and Peter M. Aronow and Michael G. Hudgens},
  journal={Annals of statistics},
  year={2021},
  volume={49 2},
  pages={
          673-701
        }
}
We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover effects. We show that estimators commonly used to estimate treatment effects under no interference are consistent for the generalized estimand for several common experimental designs under limited but otherwise arbitrary and unknown interference… Expand
Causal Inference With Interference and Noncompliance in Two-Stage Randomized Experiments
Abstract In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significantExpand
Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference
  • Alex J. Chin
  • Computer Science, Mathematics
  • Journal of Causal Inference
  • 2019
TLDR
This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments, and proposes an estimator that allows for flexible machine learning estimators to be used for fitting a nonlinear interference functional form. Expand
Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference
In many observational studies in social science and medical applications, subjects or individuals are connected, and one unit’s treatment and attributes may affect another unit’s treatment andExpand
Spillover Effects in Experimental Data
TLDR
This chapter focuses on interference in the context of randomized experiments, and considers efficient designs that allow for estimation of the treatment and spillover effects and discuss recent empirical studies that try to capture such effects. Expand
Causal Inference with Noncompliance and Unknown Interference
In this paper, we investigate a treatment effect model in which individuals interact in a social network and they may not comply with the assigned treatments. We introduce a new concept of exposureExpand
Randomization-only Inference in Experiments with Interference
In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such “interference between units” violatesExpand
Population Interference in Panel Experiments
TLDR
A general frame- work for studying population interference in panel experiments is proposed and a central limit theorem is proved under weaker conditions than previous results in the literature and the trade-off between flexibility in the design and the interference structure is highlighted. Expand
Policy design in experiments with unknown interference
TLDR
An experimental design for estimation and inference on welfaremaximizing policies in the presence of spillover effects and derives small-sample guarantees on the difference between the maximum attainable welfare and the welfare evaluated at the estimated policy. Expand
A note on Horvitz-Thompson estimators for rare subgroup analysis in the presence of interference
When there is interference, a subject's outcome depends on the treatment of others and treatment effects may take on several different forms. This situation arises often, particularly in vaccineExpand
Using Exposure Mappings as Side Information in Experiments with Interference
Exposure mappings are widely used to model potential outcomes in the presence of interference, where each unit's outcome may depend not only on its own treatment, but also on the treatment of otherExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 84 REFERENCES
On inverse probability-weighted estimators in the presence of interference
TLDR
This paper proposes a generalized inverse probability-weighted estimator and two Hájek-type stabilized weighted estimators that allow any form of interference and derives their asymptotic distributions and proposes consistent variance estimators assuming partial interference. Expand
Large Sample Randomization Inference of Causal Effects in the Presence of Interference
  • Lan Liu, M. Hudgens
  • Mathematics, Medicine
  • Journal of the American Statistical Association
  • 2014
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. Expand
On mitigating the analytical limitations of finely stratified experiments
  • Colin B. Fogarty
  • Mathematics
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology)
  • 2018
While attractive from a theoretical perspective, finely stratified experiments such as paired designs suffer from certain analytical limitations not present in block-randomized experiments withExpand
Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks
TLDR
An extended unconfoundedness assumption that accounts for interference is proposed, and new covariate-adjustment methods are developed that lead to valid estimates of treatment and interference effects in observational studies on networks. Expand
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
TLDR
It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions. Expand
Design and Analysis of Experiments in Networks: Reducing Bias from Interference
TLDR
This work evaluates methods for designing and analyzing randomized experiments under minimal, realistic assumptions compatible with broad interference, finding substantial bias reductions and, despite a bias–variance tradeoff, error reductions. Expand
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is unconfounded, that is, independent of the potential outcomes givenExpand
Elements of estimation theory for causal effects in the presence of network interference
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.Expand
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. Expand
A General Method for Detecting Interference Between Units in Randomized Experiments
Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, fewExpand
...
1
2
3
4
5
...