14 citations · 44 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
GEEPERs: Principal Stratification using Principal Scores and Stacked Estimating Equations
Adam C. Sales, Kirk P. Vanacore, Erin R. Ottmar
Principal stratification is a framework for making sense of causal effects conditioned on variables that may themselves have been affected by the treatment. For instance, in an eva…
Sequential Specification Tests to Choose a Model: A Change-Point Approach
Adam C. Sales
Researchers faced with a sequence of candidate model specifications must often choose the best specification that does not violate a testable identification assumption. One option…
Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates
Adam C Sales, Ben B Hansen, Brian Rowan
In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces "rebar," a method using high-dimen…