6 papers
PPI is the Difference Estimator: Recognizing the Survey Sampling Roots of Prediction-Powered Inference
Reagan Mozer
Prediction-powered inference (PPI) is a rapidly growing framework for combining machine learning predictions with a small set of gold-standard labels to conduct valid statistical i…
Stratified Sampling for Model-Assisted Estimation with Surrogate Outcomes
Reagan Mozer, Nicole E. Pashley, Luke Miratrix
In many randomized trials, outcomes such as essays or open-ended responses must be manually scored as a preliminary step to impact analysis, a process that is costly and limiting.…
More power to you: Using machine learning to augment human coding for more efficient inference in text-based randomized trials
Reagan Mozer, Luke Miratrix
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of intere…
Leveraging text data for causal inference using electronic health records
Reagan Mozer, Aaron R. Kaufman, Leo A. Celi +1
In studies that rely on data from electronic health records (EHRs), unstructured text data such as clinical progress notes offer a rich source of information about patient characte…
Bayesian analysis of longitudinal studies with treatment by indication
Reagan Mozer, Mark E. Glickman
It is often of interest in observational studies to measure the causal effect of a treatment on time-to-event outcomes. In a medical setting, observational studies commonly involve…
Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality
Reagan Mozer, Luke Miratrix, Aaron Russell Kaufman +1
Matching for causal inference is a well-studied problem, but standard methods fail when the units to match are text documents: the high-dimensional and rich nature of the data rend…