4 papers
Improving Variance Estimation for Covariate Adjustment with Binary Outcomes
Kaitlyn Lee, Alex Ocampo, Courtney Schiffman +3
Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its 2023 guidance when base…
Bridging Binarization: Causal Inference with Dichotomized Continuous Exposures
Kaitlyn J. Lee, Alan Hubbard, Alejandro Schuler
The average treatment effect (ATE) is a common parameter estimated in causal inference literature, but it is only defined for binary exposures. Thus, despite concerns raised by som…
Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks
Yi Li, David Mccoy, Nolan Gunter +3
Modern deep neural networks are powerful predictive tools yet often lack valid inference for causal parameters, such as treatment effects or entire survival curves. While framework…
RieszBoost: Gradient Boosting for Riesz Regression
Kaitlyn J. Lee, Alejandro Schuler
Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified t…