图 书 馆
从子样本数据中发现时间因果关系Discovering Temporal Causal Relations from Subsampled Data |
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| 课程网址: | http://videolectures.net/icml2015_zhang_subsampled_data/ |
| 主讲教师: | Kun Zhang |
| 开课单位: | 马克斯普朗克智能系统研究所 |
| 开课时间: | 2015-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Granger causal analysis has been an important tool for causal analysis for time series in various fields, including neuroscience and economics, and recently it has been extended to include instantaneous effects between the time series to explain the contemporaneous dependence in the residuals. In this paper, we assume that the time series at the true causal frequency follow the vector autoregressive model. We show that when the data resolution becomes lower due to subsampling, neither the original Granger causal analysis nor the extended one is able to discover the underlying causal relations. We then aim to answer the following question: can we estimate the temporal causal relations at the right causal frequency from the subsampled data? Traditionally this suffers from the identifiability problems: under the Gaussianity assumption of the data, the solutions are generally not unique. We prove that, however, if the noise terms are non-Gaussian, the underlying model for the high frequency data is identifiable from subsampled data under mild conditions. We then propose an Expectation-Maximization (EM) approach and a variational inference approach to recover temporal causal relations from such subsampled data. Experimental results on both simulated and real data are reported to illustrate the performance of the proposed approaches. |
| 关 键 词: | 因果分析; 瞬时效应; 模拟数据 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-19：chenjy |
| 最后编审: | 2022-12-19：chenjy |
| 阅读次数: | 14 |