3 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
Precise Unbiased Estimation in Randomized Experiments using Auxiliary Observational Data
Johann A. Gagnon-Bartsch, Adam C. Sales, Edward Wu +4
Randomized controlled trials (RCTs) are increasingly prevalent in education research, and are often regarded as a gold standard of causal inference. Two main virtues of randomized…
Separating and reintegrating latent variables to improve classification of genomic data
Nora Yujia Payne, Johann A. Gagnon-Bartsch
Genomic datasets contain the effects of various unobserved biological variables in addition to the variable of primary interest. These latent variables often affect a large number…
The P-LOOP Estimator: Covariate Adjustment for Paired Experiments
Edward Wu, Johann A. Gagnon-Bartsch
In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. Because of both the p…
The LOOP Estimator: Adjusting for Covariates in Randomized Experiments
Edward Wu, Johann Gagnon-Bartsch
When conducting a randomized controlled trial, it is common to specify in advance the statistical analyses that will be used to analyze the data. Typically these analyses will invo…
The Classification Permutation Test: A Nonparametric Test for Equality of Multivariate Distributions
Johann Gagnon-Bartsch, Yotam Shem-Tov
The gold standard for identifying causal relationships is a randomized controlled experiment. In many applications in the social sciences and medicine, the researcher does not cont…
Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed
Laurent Jacob, Johann Gagnon-Bartsch, Terence P. Speed
When dealing with large scale gene expression studies, observations are commonly contaminated by unwanted variation factors such as platforms or batches. Not taking this unwanted v…