7 papers
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…
Estimating Individual Customer Lifetime Values with R: The CLVTools Package
Markus Meierer, Patrick Bachmann, Jeffrey Näf +2
Customer lifetime value (CLV) describes a customer's long-term economic value for a business. This metric is widely used in marketing, for example, to select customers for a market…
A Practical Guide to Modern Imputation
Jeffrey Näf
This guide based on recent papers should help researchers avoid some of the most common pitfalls of missing value imputation imputation.
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…
Do we Need Dozens of Methods for Real World Missing Value Imputation?
Krystyna Grzesiak, Christophe Muller, Julie Josse +1
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…
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…