31 citations · 45 across the 14 of their papers we have counts for
5 papers · 1 filter
Statistical matching and subclassification with a continuous dose: characterization, algorithm, and application to a health outcomes study
Bo Zhang, Emily J. Mackay, Mike Baiocchi
Subclassification and matching are often used in empirical studies to adjust for observed covariates; however, they are largely restricted to relatively simple study designs with a…
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…
When black box algorithms are (not) appropriate: a principled prediction-problem ontology
Jordan Rodu, Michael Baiocchi
In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. In this paper, we introduce the term "outcome reasoning" to refer to this form of reasonin…
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…