Christopher Bockel-Rickermann

11 papers Journal 11
YearRankTypeTitle / Venue / Authors
2026 J jnl
Uplift modeling with continuous treatments: A predict-then-optimize approach.
Eur. J. Oper. Res.
Simon De Vos, Christopher Bockel-Rickermann, Stefan Lessmann, Wouter Verbeke
2025 J jnl
Can causal machine learning reveal individual bid responses of bank customers? - A study on mortgage loan applications in Belgium.
Decis. Support Syst.
Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke
2025 J jnl
Predicting Employee Turnover: Scoping and Benchmarking the State-of-the-Art.
Bus. Inf. Syst. Eng.
Simon De Vos, Christopher Bockel-Rickermann, Jente Van Belle, Wouter Verbeke
2025 J jnl
Using representation balancing to learn conditional-average dose responses from clustered data.
Trans. Mach. Learn. Res.
Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke
2024 J jnl
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation.
CoRR
Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke
2024 J jnl
Uplift modeling with continuous treatments: A predict-then-optimize approach.
CoRR
Simon De Vos, Christopher Bockel-Rickermann, Stefan Lessmann, Wouter Verbeke
2023 J jnl
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions.
CoRR
Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke
2023 J jnl
Fraud analytics: A decade of research: Organizing challenges and solutions in the field.
Expert Syst. Appl.
Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke
2023 J jnl
Learning continuous-valued treatment effects through representation balancing.
CoRR
Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke
2022 J jnl
Fraud Analytics: A Decade of Research - Organizing Challenges and Solutions in the Field.
CoRR
Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke
2022 J jnl
Predicting Day-Ahead Stock Returns using Search Engine Query Volumes: An Application of Gradient Boosted Decision Trees to the S&P 100.
CoRR
Christopher Bockel-Rickermann