AI RESEARCH
Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports
arXiv CS.LG
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ArXi:2603.11546v1 Announce Type: new Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multiple related tasks where part of the forward causal mechanism is invariant across tasks, while other components are task-specific. We propose Multi-Task Anti-Causal learning (MTAC), a framework for estimating causes from outcomes and confounders by explicitly exploiting such cross-task invariances.