图 书 馆
通过独立成分分析发现循环因果模型Discovering Cyclic Causal Models by Independent Components Analysis |
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| 课程网址: | http://videolectures.net/cmulls08_lacerda_dcc/ |
| 主讲教师: | Gustavo Lacerda |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-02-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This talk will start by presenting Shimizu et al's (2006) ICA-based approach (LiNGAM) for discovering acyclic (DAG) linear Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. This is remarkable because it determines the direction of every causal arrow when no experimental data is available. Our work generalizes the above. By relaxing the acyclicity constraint, our approach, LiNG-DG, enables the discovery of arbitrary directed graph (DG) linear SEMs. We present various algorithm sketches for causal discovery with LiNG-DG, and show results of simulation for one such algorithm. When the error terms are non-Gaussian, LiNG-DG discovery algorithms output a smaller set of candidate SEMs than Richardson's Cyclic Causal Discovery (CCD) algorithm. We prove that all the models output by LiNG-DG entail the same observational distribution and are equally simple (i.e. same number of edges). This implies that without further assumptions, no algorithm can reliably narrow the set of candidate SEMs output by LiNG-DG using just observational data. However, we show that under the additional assumption of "stability", the set of candidate models output by LiNG-DG can be further narrowed down (under some conditions, to a single model). |
| 关 键 词: | 独立分量分析; 算法; 模型; 机械学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 53 |