5 citations · 12 across the 9 of their papers we have counts for
6 papers · 1 filter
Estimation of Causal Effects Under K-Nearest Neighbors Interference
Samirah Alzubaidi, Michael J. Higgins
Considerable recent work has focused on methods for analyzing experiments which exhibit treatment interference -- that is, when the treatment status of one unit may affect the resp…
Detecting Treatment Interference under the K-Nearest-Neighbors Interference Model
Samirah H. Alzubaidi, Michael J. Higgins
We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods…
The Benefits of Probability-Proportional-to-Size Sampling in Cluster-Randomized Experiments
Yeng Xiong, Michael J. Higgins
In a cluster-randomized experiment, treatment is assigned to clusters of individual units of interest--households, classrooms, villages, etc.--instead of the units themselves. The…
From one environment to many: The problem of replicability of statistical inferences
James J. Higgins, Michael J. Higgins, Jinguang Lin
Among plausible causes for replicability failure, one that has not received sufficient attention is the environment in which the research is conducted. Consisting of the population…
Generalized full matching and extrapolation of the results from a large-scale voter mobilization experiment
Fredrik Sävje, Michael J. Higgins, Jasjeet S. Sekhon
Matching is an important tool in causal inference. The method provides a conceptually straightforward way to make groups of units comparable on observed characteristics. The use of…
Blocking estimators and inference under the Neyman-Rubin model
Michael J. Higgins, Fredrik Sävje, Jasjeet S. Sekhon
We derive the variances of estimators for sample average treatment effects under the Neyman-Rubin potential outcomes model for arbitrary blocking assignments and an arbitrary numbe…