图 书 馆
因果推理：从干预效果到具有部分可观察性的学习和推理。Causal inference: from effects of interventions to learning and inference with partial observability. |
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| 课程网址: | http://videolectures.net/uai2011_shpitser_causal/ |
| 主讲教师: | Ilya Shpitser |
| 开课单位: | 哈佛大学 |
| 开课时间: | 2011-08-24 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Establishing cause-effect relationships is fundamental to progress of empirical science. A general mathematical theory of causation which supports human causal intuitions is thus of utmost importance for formalizing and perhaps one day automating scientific inquiry. This tutorial will describe one such theory of causation, based on graphical models. The tutorial will consist of two parts. The first part will introduce graphical causal models as vehicles for expressing causal assumptions, and interventions as an operation formalizing the notions of causal effects and counterfactuals. Examples of using this framework to pose and answer practical causal questions in public health will be given. The first part will conclude by giving a general algorithm for identifying causal effects from observational studies, and showing how the so called post-truncation independence constraints in latent variable graphical models can be given an interpretation in terms of such effects. The second part will discuss a recursive factorization (r-factorization) for latent variable graphical models, and show how post-truncation constraints which do not make an appearance in directed acyclic graph (DAG) models are captured by this factorization. A causal interpretation of this factorization will be given. The second part will conclude by giving some notable examples of such models, including testably distinct models which agree on all conditional independences, and models with a graph fully specified by a single post-truncation independence. |
| 关 键 词: | 因果关系; 通用数学理论; 图形模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-04：cjy |
| 阅读次数: | 87 |