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Found 3 papers, 0 papers with code
On Undersmoothing and Sample Splitting for Estimating a Doubly Robust Functional
We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature.
Revisiting the g-null paradox
An often cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed under the conditions that motivate its use (i. e., when identifiability conditions hold and measured time-varying confounders are affected by past treatment).
Causal Inference
Methodology
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can help them decide if and how to incorporate model predictions into their decision making.
