5 papers
Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach
Nandini Murali, Keith Barnatchez, Jordana E. Hoppe +3
Exposure misclassification is a common issue prevalent in studies of respiratory infections in cystic fibrosis. Throat swabs are frequently used in place of expectorated or induced…
Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding
Keith Barnatchez, Kevin P. Josey, Rachel C. Nethery +1
Data-driven decision making frequently relies on predicting counterfactual outcomes. In practice, researchers commonly train counterfactual prediction models on a source dataset to…
Transporting Predictions via Double Machine Learning: Predicting Partially Unobserved Students' Outcomes
Falco J. Bargagli-Stoffi, Emma Landry, Kevin P. Josey +3
Educational policymakers often lack data on student outcomes where standardized tests were not administered. Machine learning can predict unobserved outcomes in target populations…
Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
Keith Barnatchez, Kevin P. Josey, Nima S. Hejazi +3
Measurement error is a common challenge for causal inference studies using electronic health record (EHR) data, where clinical outcomes and treatments are frequently mismeasured. R…
Equity in the Distribution of Regulatory PM2.5 Monitors
Zoé Haskell-Craig, Kevin P. Josey, Patrick L. Kinney +1
Unequal exposure to air pollution by race and socioeconomic status is well-documented in the U.S. However, there has been relatively little research on inequities in the collection…