1 citations · 1 across the 6 of their papers we have counts for
6 papers
Covariate Adjustment in Randomized Experiments: A Unified Framework for Decision and Practice
Jiawei Fu, Donald P. Green
Should researchers adjust for covariates in randomized experiments, and if so, how? The literature offers three distinct prescriptions: do not adjust because randomization guarante…
Nonparametric Identification and Estimation of Causal Effects on Latent Outcomes
Jiawei Fu, Donald P. Green
How can we conduct causal inference when the outcome of interest is latent and imperfectly measured by multiple indicators? We develop a nonparametric framework for identifying and…
The Illusion of Learning from Observational Data: An Empirical Bayes Perspective
Bohan Wu, Sebastian Salazar, Donald P. Green +1
Randomized experiments have long been the gold standard for scientists seeking to learn about cause and effect. When randomized experiments are infeasible, scientists often resort…
The Sequential Nature of Science: Quantifying Learning from a Sequence of Studies
Jonas M. Mikhaeil, Donald P. Green, David Blei
Scientific progress is inherently sequential: collective knowledge is updated as new studies enter the literature. We propose the sequential meta-analysis research trace (SMART), w…
Quantifying How Much Has Been Learned from a Research Study
Jonas M. Mikhaeil, Donald P. Green
How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we…
Sharp Bounds on the Variance of General Regression Adjustment in Randomized Experiments
Jonas M. Mikhaeil, Donald P. Green
A growing statistical literature focuses on causal inference in the context of experiments where the target of inference is the average treatment effect in a finite population and…