1.2k citations · 1.2k across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…