CausalMan: A physics-based simulator for large-scale causality

2025-02-18

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

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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

• Develops realistic causal models for benchmarking
• Simulates complex behaviors in production lines
• Evaluates performance of causality analysis methods

Innovation

• Physics-based causality simulator
• Linear and non-linear mechanisms
• Large-scale performance evaluation