图 书 馆
因果发现的广义评分函数Generalized Score Functions for Causal Discovery |
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| 课程网址: | http://videolectures.net/kdd2018_huang_causal_discovery/ |
| 主讲教师: | Biwei Huang |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Discovery of causal relationships from observational data is a fundamental problem. Roughly speaking, there are two types of methods for causal discovery, constraint-based ones and score-based ones. Score-based methods avoid the multiple testing problem and enjoy certain advantages compared to constraint-based ones. However, most of them need strong assumptions on the functional forms of causal mechanisms, as well as on data distributions, which limit their applicability. In practice the precise information of the underlying model class is usually unknown. If the above assumptions are violated, both spurious and missing edges may result. In this paper, we introduce generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes. In particular, we exploit regression in RKHS to capture the dependence in a nonparametric way. The resulting causal discovery approach produces asymptotically correct results in rather general cases, which may have nonlinear causal mechanisms, a wide class of data distributions, mixed continuous and discrete data, and multidimensional variables. Experimental results on both synthetic and real-world data demonstrate the efficacy of our proposed approach. |
| 关 键 词: | 发现因果关系; 因果发现方法; 基础模型类的精确信息; RKHS中的回归 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-28：cyh |
| 最后编审: | 2023-01-28：cyh |
| 阅读次数: | 32 |