38 citations · 61 across the 7 of their papers we have counts for
7 papers
Bias correction in treatment effect estimates following data-driven biomarker cutoff selection
Chi Zhang, Wei Shi, Spencer Woody +1
Predictive biomarkers are playing an essential role in precision medicine. Identifying an optimal cutoff to select patient subsets with greater benefit from treatment is critical a…
Estimating heterogeneous effects of continuous exposures using Bayesian tree ensembles: revisiting the impact of abortion rates on crime
Spencer Woody, Carlos M. Carvalho, P. Richard Hahn +1
In estimating the causal effect of a continuous exposure or treatment, it is important to control for all confounding factors. However, most existing methods require parametric spe…
Bayesian inference for treatment effects under nested subsets of controls
Spencer Woody, Carlos M. Carvalho, Jared S. Murray
When constructing a model to estimate the causal effect of a treatment, it is necessary to control for other factors which may have confounding effects. Because the ignorability as…
Bayesian Model Calibration for Extrapolative Prediction via Gibbs Posteriors
Spencer Woody, Novin Ghaffari, Lauren Hund
The current standard Bayesian approach to model calibration, which assigns a Gaussian process prior to the discrepancy term, often suffers from issues of unidentifiability and comp…
Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge
Carlos Carvalho, Avi Feller, Jared Murray +2
A growing number of methods aim to assess the challenging question of treatment effect variation in observational studies. This special section of "Observational Studies" reports t…
Model interpretation through lower-dimensional posterior summarization
Spencer Woody, Carlos M. Carvalho, Jared S. Murray
Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven…
Optimal post-selection inference for sparse signals: a nonparametric empirical-Bayes approach
Spencer Woody, Oscar Hernan Madrid Padilla, James G. Scott
Many recently developed Bayesian methods have focused on sparse signal detection. However, much less work has been done addressing the natural follow-up question: how to make valid…