2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
Jeffrey Näf, Junhyung Park, Herbert Susmann
The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the effect of a treatment conditional on covariates. However, the CATE wi…
Parametric MMD Estimation with Missing Values: Robustness to Missingness and Data Model Misspecification
Badr-Eddine Chérief-Abdellatif, Jeffrey Näf
In the missing data literature, the Maximum Likelihood Estimator (MLE) is celebrated for its ignorability property under missing at random (MAR) data. However, its sensitivity to m…
How to rank imputation methods?
Jeffrey Näf, Krystyna Grzesiak, Erwan Scornet
Imputation is an attractive tool for dealing with the widespread issue of missing values. Consequently, studying and developing imputation methods has been an active field of resea…