3 citations · 7 across the 11 of their papers we have counts for
11 papers
A General Framework for Design-Based Treatment Effect Estimation in Paired Cluster-Randomized Experiments
Charlotte Z. Mann, Adam C. Sales, Johann A. Gagnon-Bartsch
Paired cluster-randomized experiments (pCRTs) are common in education program impact evaluation trials. Although common, there is surprisingly no clear consensus regarding how to a…
Building A Theoretical Foundation for Combining Negative Controls and Replicates
Jiming Jiang, Johann A. Gagnon-Bartsch, Terence P. Speed
Studies using assays to quantify the expression of thousands of genes on tens to thousands of cell samples have been carried out for over 20 years. Such assays are based on microar…
Combining observational and experimental data for causal inference considering data privacy
Charlotte Z. Mann, Adam C. Sales, Johann A. Gagnon-Bartsch
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data pri…
Using Auxiliary Data to Boost Precision in the Analysis of A/B Tests on an Online Educational Platform: New Data and New Results
Adam C. Sales, Ethan B. Prihar, Johann A. Gagnon-Bartsch +1
Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without co…
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…
A Review of Containerization for Interactive and Reproducible Analysis
Gregory J. Hunt, Johann A. Gagnon-Bartsch
In recent decades the analysis of data has become increasingly computational. Correspondingly, this has changed how scientific and statistical work is shared. For example, it is no…
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…