3 papers
cs.LG2026
Adversarial Causal Tuning for Realistic Time-series Generation
Nikolaos Gkorgkolis, Nikolaos Kougioulis, MingXue Wang +4
We address the problem of generating simulated, yet realistic, time-series data from a causal model with the same observational and interventional distributions as a given real dat…
cs.LG2026
Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning
Dario Simionato, Andrea Tonon, Mingxue Wang +3
In streaming platforms churn is extremely costly, yet A/B tests are typically evaluated using outcomes observed within a limited experimental horizon. Even when both short- and pre…
cs.LG2026
Large Causal Models for Temporal Causal Discovery
Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang +4
Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an…