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stat.ME2024
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…
math.ST2024★ 2 cited
What Is a Good Imputation Under MAR Missingness?
Jeffrey Näf, Erwan Scornet, Julie Josse
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…