1.2k citations · 1.2k across the 13 of their papers we have counts for
14 papers
The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
Aaron Fisher
Many methods for estimating conditional average treatment effects (CATEs) can be expressed as weighted pseudo-outcome regressions (PORs). Previous comparisons of POR techniques hav…
Three-way Cross-Fitting and Pseudo-Outcome Regression for Estimation of Conditional Effects and other Linear Functionals
Aaron Fisher, Virginia Fisher
We propose an approach to better inform treatment decisions at an individual level by adapting recent advances in average treatment effect estimation to conditional average treatme…
Gaussian Latent Dirichlet Allocation for Discrete Human State Discovery
Congyu Wu, Aaron Fisher, David Schnyer
In this article we propose and validate an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for the problem of discrete state discovery from repeated,…
Online Control of the False Discovery Rate under "Decision Deadlines"
Aaron Fisher
Online testing procedures aim to control the extent of false discoveries over a sequence of hypothesis tests, allowing for the possibility that early-stage test results influence t…
Online False Discovery Rate Control for LORD & SAFFRON Under Positive, Local Dependence
Aaron Fisher
Online testing procedures assume that hypotheses are observed in sequence, and allow the significance thresholds for upcoming tests to depend on the test statistics observed so far…
Optimizing Rescoring Rules with Interpretable Representations of Long-Term Information
Aaron Fisher
Analyzing temporal data (e.g., wearable device data) requires a decision about how to combine information from the recent and distant past. In the context of classifying sleep stat…
Treatment Effect Bias from Sample Snooping: Blinding Outcomes is Neither Necessary nor Sufficient
Aaron Fisher
Popular guidance on observational data analysis states that outcomes should be blinded when determining matching criteria or propensity scores. Such a blinding is informally said t…
Visually Communicating and Teaching Intuition for Influence Functions
Aaron Fisher, Edward H. Kennedy
Estimators based on influence functions (IFs) have been shown to be effective in many settings, especially when combined with machine learning techniques. By focusing on estimating…
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron Fisher, Cynthia Rudin, Francesca Dominici
Variable importance (VI) tools describe how much covariates contribute to a prediction model's accuracy. However, important variables for one well-performing model (for example, a…
Fast Out-of-Sample Predictions for Bayesian Hierarchical Models of Latent Health States
Aaron J Fisher, R Yates Coley, Scott L Zeger
Hierarchical Bayesian models can be especially useful in precision medicine settings, where clinicians are interested in estimating the patient-level latent variables associated wi…
A Bayesian Hierarchical Model for Prediction of Latent Health States from Multiple Data Sources with Application to Active Surveillance of Prostate Cancer
R. Yates Coley, Aaron J. Fisher, Mufaddal Mamawala +3
In this article, we present a Bayesian hierarchical model for predicting a latent health state from longitudinal clinical measurements. Model development is motivated by the need t…
Fast, Exact Bootstrap Principal Component Analysis for p>1 million
Aaron Fisher, Brian Caffo, Brian Schwartz +1
Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subjec…
A randomized trial in a massive online open course shows people don't know what a statistically significant relationship looks like, but they can learn
Aaron Fisher, G. Brooke Anderson, Roger Peng +1
Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely…
interAdapt -- An Interactive Tool for Designing and Evaluating Randomized Trials with Adaptive Enrollment Criteria
Aaron Fisher, Harris Jaffee, Michael Rosenblum
The interAdapt R package is designed to be used by statisticians and clinical investigators to plan randomized trials. It can be used to determine if certain adaptive designs offer…