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cs.LG2025
Estimating Treatment Effects in Networks under Unknown Exposure Mappings
Daan Caljon, Jente Van Belle, Wouter Verbeke
Estimating heterogeneous treatment effects in network settings is complicated by interference, meaning that the outcome of an instance can be influenced by the treatment status of…
cs.LG2024
Using dynamic loss weighting to boost improvements in forecast stability
Daan Caljon, Jeff Vercauteren, Simon De Vos +2
Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an ex…
cs.LG2024
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck +1
Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariat…