Data Science and Data Management

Projects, Products, Practice, and Pitfalls: A Hands-On Approach to Advanced Data Analytics

Julia Lane, Rayid Ghani, Frauke Kreuter, Anna-Carolina Haensch, and Maryah Garner

Insufficiently justified disparate impact: a new criterion for fair recommendations

Neil Menghani, Edward McFowland III, and Daniel B. Neill

Fairness and bias of machine learning approaches for diabetes screening in the Emergency Department

Isaac Bohart, [...], Daniel B. Neill*, David Lee*

Identifying housing-sensitive health conditions using machine learning

O. Chakraborty, K. Dragan, I. Ellen, S. Glied, R. A. Howland, S. S. Wang, and D. B. Neill

Auditing predictive models for intersectional biases

Kate Boxer, Edward McFowland III, and Daniel B. Neill

Efficient discovery of heterogeneous treatment effects in randomized experiments via anomalous pattern detection

Edward McFowland III, Sriram Somanchi, and Daniel B. Neill

Neighborhood-level risk factors for severe hyperglycemia among ED patients without a prior diabetes diagnosis

Christian Koziatek, Isaac Bohart, [...], Daniel B. Neill, David Lee

PROVIDENT: development and validation of a machine learning model to predict neighborhood-level overdose risk in Rhode Island

Bennett Allen, [...], Magdalena Cerda, Daniel B. Neill

Fairness and bias of machine learning in healthcare and medicine

Isaac Bohart, Daniel B. Neill, and David Lee

Preventing overdose using information and data from the environment (PROVIDENT): Protocol for a randomised, population-based, community intervention trial

B. D. L. Marshall, N. Alexander-Scott, J. L. Yedinak, B. Hallowell, W. C. Goedel, B. Allen, R. C. Schell, M. S. Krieger, C. Pratty, J. Ahern, D. B. Neill, and M. Cerda

Provable detection of propagating sampling bias in prediction models

Pavan Ravishankar, Qingyu Mo, Edward McFowland III, and Daniel B. Neill

Calibrated nonparametric scan statistics for anomalous pattern detection in graphs

Chunpai Wang, Daniel B. Neill, and Feng Chen