2 citations · 5 across the 7 of their papers we have counts for
10 papers
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection
Conor K. Corbin, Michael Baiocchi, Jonathan H. Chen
When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a sub…
Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics
Evan T. R. Rosenman, Rina Friedberg, Mike Baiocchi
We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported, and will involve sensitive topics. Our interest is in misreport…
Assignment-Control Plots: A Visual Companion for Causal Inference Study Design
Rachael C. Aikens, Michael Baiocchi
An important step for any causal inference study design is understanding the distribution of the treated and control subjects in terms of measured baseline covariates. However, not…
A Causal Machine Learning Framework for Predicting Preventable Hospital Readmissions
Ben J. Marafino, Alejandro Schuler, Vincent X. Liu +2
Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies--that is, to enable interventions to be targeted to the patients who wil…
Understanding the spatial burden of gender-based violence: Modelling patterns of violence in Nairobi, Kenya through geospatial information
Rina Friedberg, Clea Sarnquist, Gavin Nyairo +2
We present statistical techniques for analyzing global positioning system (GPS) data in order to understand, communicate about, and prevent patterns of violence. In this pilot stud…
stratamatch: Prognostic ScoreStratification using a Pilot Design
Rachael C. Aikens, Joseph Rigdon, Justin Lee +5
Optimal propensity score matching has emerged as one of the most ubiquitous approaches for causal inference studies on observational data; However, outstanding critiques of the sta…