Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies

@article{Hansen2014ClusteredTA,
  title={Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies},
  author={B. Hansen and P. Rosenbaum and Dylan S. Small},
  journal={Journal of the American Statistical Association},
  year={2014},
  volume={109},
  pages={133 - 144}
}
Clustered treatment assignment occurs when individuals are grouped into clusters prior to treatment and whole clusters, not individuals, are assigned to treatment or control. In randomized trials, clustered assignments may be required because the treatment must be applied to all children in a classroom, or to all patients at a clinic, or to all radio listeners in the same media market. The most common cluster randomized design pairs 2S clusters into S pairs based on similar pretreatment… Expand
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References

SHOWING 1-10 OF 75 REFERENCES
Sensitivity analysis for certain permutation inferences in matched observational studies
SUMMARY In observational studies, treatments are not randomly assigned to experimental units, so that randomization tests and their associated interval estimates are not generally applicable. In anExpand
Randomization Inference in a Group–Randomized Trial of Treatments for Depression
In the Prospect Study, in 10 pairs of two primary-care practices, one practice was picked at random to receive a “depression care manager” to treat its depressed patients. Randomization inference,Expand
Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign
TLDR
This article addresses all of the complications of Gerber and Green’s study of voter turnout campaigns using methods in the tradition of Fisher and Neyman, and merges recent developments in randomization-based inference for comparative studies with somewhat older developments in design-based analysis of sample surveys. Expand
Estimating causal effects of treatments in randomized and nonrandomized studies.
A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimatingExpand
Matching methods for causal inference: A review and a look forward.
  • E. Stuart
  • Computer Science, Medicine
  • Statistical science : a review journal of the Institute of Mathematical Statistics
  • 2010
TLDR
A structure for thinking about matching methods and guidance on their use is provided, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed. Expand
Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome
This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The techniqueExpand
Testing one hypothesis twice in observational studies
In a matched observational study of treatment effects, a sensitivity analysis asks about the magnitude of the departure from random assignment that would need to be present to alter the conclusionsExpand
Amplification of Sensitivity Analysis in Matched Observational Studies
TLDR
A sensitivity analysis displays the increase in uncertainty that attends an inference when a key assumption is relaxed and an amplification of a sensitivity analysis is defined as a map from each point in a low-dimensional sensitivity analysis to a set of points in a higher dimensional sensitivity analysis such that the possible inferences are the same for all points in the set. Expand
Covariance Adjustment in Randomized Experiments and Observational Studies
By slightly reframing the concept of covariance adjustment in randomized experiments, a method of exact permutation inference is derived that is entirely free of distributional assumptions and usesExpand
Sensitivity to Exogeneity Assumptions in Program Evaluation
In many empirical studies of the effect of social programs researchers assume that, conditional on a set of observed covariates, assignment to the treatment is exogenous or unconfounded (akaExpand
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