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This paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that leverages cross-task invariances in anti-causal problems to infer latent causes from observed effects and confounders. MTAC performs causal discovery to learn a shared causal graph and then uses a structured multi-task structural equation model (SEM) to factorize the outcome-generation process into task-invariant and task-specific mechanisms. Applied to urban event reconstruction from resident reports across three tasks (parking violations, abandoned properties, and unsanitary conditions), MTAC achieves up to 34.61% MAE reduction compared to baselines.
Reconstructing urban events from resident reports gets a serious boost with a new method that learns transferable causal mechanisms across different types of incidents.
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. MTAC first performs causal discovery to learn a shared causal graph and then instantiates a structured multi-task structural equation model (SEM) that factorizes the outcome-generation process into (i) a task-invariant mechanism and (ii) task-specific mechanisms via a shared backbone with task-specific heads. Building on the learned forward model, MTAC performs maximum A posteriori (MAP)based inference to reconstruct causes by jointly optimizing latent mechanism variables and cause magnitudes under the learned causal structure. We evaluate MTAC on the application of urban event reconstruction from resident reports, spanning three tasks:parking violations, abandoned properties, and unsanitary conditions. On real-world data collected from Manhattan and the city of Newark, MTAC consistently improves reconstruction accuracy over strong baselines, achieving up to 34.61\% MAE reduction and demonstrating the benefit of learning transferable causal mechanisms across tasks.
