通过抽象实现因果关系：利用大语言模型在多变量时间序列中进行符号规则学习
Causality by Abstraction: Symbolic Rule Learning in Multivariate Timeseries with Large Language Models
AI总结：
ruleXplain利用大语言模型提取多变量时间序列中的因果规则，通过生成反事实输入轨迹和闭环细化确保规则的可验证性和有效性。
摘要：
在时间序列数据中推断因果关系是一个基本挑战，尤其是在底层系统表现出复杂动态且无法通过简单函数映射捕捉的情况下。传统方法往往无法产生通用且可解释的解释，因为多个不同的输入轨迹可能产生几乎无法区分的输出。在本工作中，我们提出了ruleXplain框架，该框架利用大语言模型（LLMs）来提取输入输出关系的正式解释。我们的方法引入了一种受约束的符号规则语言，包含时间运算符和延迟语义，使LLMs能够通过结构化提示生成可验证的因果规则。ruleXplain依赖于一个原理性的模型（例如模拟器）将多变量输入时间序列映射到输出时间序列。在ruleXplain中，模拟器用于生成多样化的反事实输入轨迹，这些轨迹产生相似的目标输出，作为候选解释。这些反事实输入被聚类并作为上下文提供给LLM，其任务是生成符号规则，编码导致输出时间序列中可观察模式的联合时间趋势。闭环细化过程确保规则的一致性和语义有效性。我们通过PySIRTEM流行病模拟器验证该框架，将测试率输入映射到每日感染人数；并通过EnergyPlus建筑能源模拟器，观察温度和太阳辐射输入对电力需求的影响。为了验证，我们进行了三类实验：（1）通过输入重建评估规则集的有效性；（2）消融研究评估规则集的因果编码；（3）测试提取的规则在不同相位动态下的泛化能力。
英文摘要：
Inferring causal relations in timeseries data with delayed effects is a fundamental challenge, especially when the underlying system exhibits complex dynamics that cannot be captured by simple functional mappings. Traditional approaches often fail to produce generalized and interpretable explanations, as multiple distinct input trajectories may yield nearly indistinguishable outputs. In this work, we present ruleXplain, a framework that leverages Large Language Models (LLMs) to extract formal explanations for input-output relations in simulation-driven dynamical systems. Our method introduces a constrained symbolic rule language with temporal operators and delay semantics, enabling LLMs to generate verifiable causal rules through structured prompting. ruleXplain relies on the availability of a principled model (e.g., a simulator) that maps multivariate input time series to output time series. Within ruleXplain, the simulator is used to generate diverse counterfactual input trajectories that yield similar target output, serving as candidate explanations. Such counterfactual inputs are clustered and provided as context to the LLM, which is tasked with the generation of symbolic rules encoding the joint temporal trends responsible for the patterns observable in the output times series. A closed-loop refinement process ensures rule consistency and semantic validity. We validate the framework using the PySIRTEM epidemic simulator, mapping testing rate inputs to daily infection counts; and the EnergyPlus building energy simulator, observing temperature and solar irradiance inputs to electricity needs. For validation, we perform three classes of experiments: (1) the efficacy of the ruleset through input reconstruction; (2) ablation studies evaluating the causal encoding of the ruleset; and (3) generalization tests of the extracted rules across unseen output trends with varying phase dynamics.
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