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Session MC-31: Causal Machine Learning in stream Analytics
Monday, 12:30-14:00Room: 046 (building: 208)
| Session chair(s): |
|
| 2321. A predict-then-optimize approach for uplift modeling with continuous individual treatment effects |
Simon De Vos
[R] - Belgium | accepted | ||
| Wouter Verbeke
[R] - Belgium | ||||
| 2834. Exploring Learning to Rank for Optimal Treatment Allocation |
Toon Vanderschueren
[R] - Belgium | accepted | ||
| Wouter Verbeke
[R] - Belgium | ||||
| Felipe Moraes
[] - Netherlands | ||||
| Hugo Proença
[] - Netherlands | ||||
| 2174. Optimizing Treatment Allocation in the Presence of Interference: a Causal Machine Learning Approach to the Influence Maximization Problem |
Daan Caljon
[R] - Belgium | accepted | ||
| Jente Van Belle
[] - Belgium | ||||
| Wouter Verbeke
[R] - Belgium | ||||
| 2325. Estimating continuous treatment effects from observational data: An empirical evaluation of methods, scenarios, and challenges |
Christopher Bockel-Rickermann
[R] - Belgium | accepted | ||
| Tim Verdonck
[] - Belgium | ||||
| Wouter Verbeke
[R] - Belgium | ||||