Search Results for author:
Found 6 papers, 2 papers with code
Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
We decompose the ATE estimand as the difference between a pooled-ATE estimand that integrates RCT and RWD and a bias estimand that captures the conditional effect of RCT enrollment on the outcome.
Self-Consistent Conformal Prediction
Conformal prediction helps decision-makers quantify uncertainty in point predictions of outcomes, allowing for better risk management for actions.
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk.
Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data
Given a web-based calibration set, we apply conformal prediction with a novel conformity score that accounts for potential errors in retrieved web data.
Adaptive debiased machine learning using data-driven model selection techniques
For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions.
Causal isotonic calibration for heterogeneous treatment effects
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects.
