Causal discovery, i.e., inferring underlying cause-effect relationships from observations of a scene or system, is an inherent mechanism in human cognition, but has been shown to be highly challenging to automate. The majority of approaches in the literature aiming for this task consider constrained scenarios with fully observed variables or data from stationary time-series. In this work we aim for causal discovery in a more general class of scenarios, scenes with non-stationary behavior over time. For our purposes we here regard a scene as a composition objects interacting with each other over time. Non-stationarity is modeled as stationarity conditioned on an underlying variable, a state, which can be of varying dimension, more or less hidden given observations of the scene, and also depend more or less directly on these observations. We propose a probabilistic deep learning approach called State-Dependent Causal Inference (SDCI) for causal discovery in such conditionally stationary time-series data. Results in two different synthetic scenarios show that this method is able to recover the underlying causal dependencies with high accuracy even in cases with hidden states.
翻译:致因发现,即从对场景或系统的观测中推断出潜在因果关系,是人类认知的固有机制,但已证明对自动化极具挑战性。本任务文献中大多数旨在完成这项任务的方法都考虑到带有完全观察变量或来自静止时间序列的数据的有限假设情景。在这项工作中,我们的目标是在更一般的情景类别中,在具有非静止行为的场景中进行因果发现。为了我们的目的,我们在这里将场景视为一个在一段时间内相互作用的构成物体。非静止性被模拟为以基本变量为条件的静止性,这种状态可以是不同维度的,或多或少是隐藏的场景观测,也可以或多或少是直接依赖这些观测。我们建议了一种叫作 " 国家依赖因果关系 " (SDCI)的概率深刻学习方法,用于在这种有条件的固定时间序列数据中进行因果发现。两种不同的合成假设的结果表明,这一方法能够以高度精确性恢复基本因果关系,即使是在隐藏状态下。