6 citations · 12 across the 4 of their papers we have counts for
4 papers
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest
Daniel Jacob
Estimating a causal effect from observational data can be biased if we do not control for self-selection. This selection is based on confounding variables that affect the treatment…
Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects
Daniel Jacob
We investigate the finite sample performance of sample splitting, cross-fitting and averaging for the estimation of the conditional average treatment effect. Recently proposed meth…
Group Average Treatment Effects for Observational Studies
Daniel Jacob
The paper proposes an estimator to make inference of heterogeneous treatment effects sorted by impact groups (GATES) for non-randomised experiments. The groups can be understood as…
Affordable Uplift: Supervised Randomization in Controlled Experiments
Johannes Haupt, Daniel Jacob, Robin M. Gubela +1
Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decisi…