从时间数据到动态因果模型

O. Balabanov
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引用次数: 0

摘要

我们简要回顾了动态因果模型的数据推断。矢量自回归模型是我们最感兴趣的。概述了时间数据和时间序列数据的测量体系结构、表示和方法。我们认为,对数据特征的要求应来自手头动态过程的性质和模型推理的目标。为了描述和评价时间数据,人们可以使用经度、测量频率等术语。数据测量频率是推断模型是否合理的关键因素。数据经度和观测时段持续时间可通过几个时间视界表示,如最近视界、两步视界、影响可达性视界、振荡视界和进化视界。为了从数据中证明动态因果模型的推断是正确的,分析人员需要假设动态过程是平稳的,或者至少服从结构规则。动态因果模型推理任务的主要特点是已知变量的时间顺序和一定的结构规律性。在最大影响滞后未知的情况下,动态因果模型的推理还面临着额外的问题。我们考察了格兰杰的因果关系概念,并概述了其在实际情况中的不足。本文认为格兰杰因果关系作为因果发现的实用工具是不正确的。相反,边缘方向的某些规则(包含在已知的基于约束的模型推理算法中)可以揭示无混淆的因果关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From temporal data to dynamic causal models
We present a brief review of dynamic causal model inference from data. A vector autoregressive models is of our prime interest. The architecture, representation and schemes of measurement of temporal data and time series data are outlined. We argue that require- ment to data characteristics should come from the nature of dynamic process at hand and goals of model inference. To describe and evaluate temporal data one may use terms of longitude, measurement frequency etc. Data measurement frequency is crucial factor in order to an inferred model be adequate. Data longitude and observation session duration may be expressed via several temporal horizons, such as closest horizon, 2-step horizon, influence attainability horizon, oscillatory horizon, and evolutionary horizon. To justify a dynamic causal model inference from data, analyst needs to assume the dynamic process is stationary or at least obeys structural regularity. The main specificity of task of dynamic causal model inference is known temporal order of variables and certain structural regularity. If maximal lag of influence is unknown, inference of dynamic causal model faces additional problems. We examine the Granger’s causality concept and outline its deficiency in real circumstances. It is argued that Granger causality is incorrect as practical tool of causal discovery. In contrast, certain rules of edge orientation (included in known constraint-based algorithms of model inference) can reveal unconfounded causal relationship.
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