Technology Category
Machine Learning: Causal LearningReasoning under Uncertainty: CausalityCognitive Modeling & Cognitive Systems: Simulating Human Behavior
CausalMan: A physics-based simulator for large-scale causality
🤖 AI Summary
Real-world causal modeling faces challenges due to the absence of ground-truth data-generating mechanisms and difficulties in fair, reproducible evaluation. To address this, we propose CausalMan—the first physics-driven, large-scale causal simulator tailored for industrial production lines. It unifies differential equations, stochastic processes, and discrete-event system modeling to support linear/nonlinear mechanisms, modality shifts, causal graph injection, and counterfactual interventions. Its key contributions are: (1) the first high-fidelity, industrial-grade causal dynamic simulator with explicitly embedded, interpretable physical laws and abrupt behavioral transitions; and (2) the establishment of a large-scale, reproducible, and controllable benchmark platform for causal inference—previously unavailable. Leveraging CausalMan, we construct two standardized datasets and conduct systematic evaluation of mainstream causal discovery algorithms across accuracy, runtime, and memory overhead, revealing significant scalability and robustness limitations in existing methods.
Technology Category
Application Category
📝 Abstract
A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known data generating processes complicates fair benchmarking. In this paper, we present the CausalMan simulator, modeled after a real-world production line. The simulator features a diverse range of linear and non-linear mechanisms and challenging-to-predict behaviors, such as discrete mode changes. We demonstrate the inadequacy of many state-of-the-art approaches and analyze the significant differences in their performance and tractability, both in terms of runtime and memory complexity. As a contribution, we will release the CausalMan large-scale simulator. We present two derived datasets, and perform an extensive evaluation of both.
Problem
Research questions and friction points this paper is trying to address.
Develops realistic causal models for benchmarking
Simulates complex behaviors in production lines
Evaluates performance of causality analysis methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-based causality simulator
Linear and non-linear mechanisms
Large-scale performance evaluation
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