图 书 馆
因果推理：从干预的效果到部分可观察的学习和推理。Causal inference: from effects of interventions to learning and inference with partial observability. |
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| 课程网址: | http://videolectures.net/Top/Computer_Science/Artificial_Intellig... |
| 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: |  建立因果关系是经验科学发展的基础。因此，一种支持人类因果直觉的因果关系的通用数学理论对于形式化（也许有一天使科学探究自动化）至关重要。本教程将基于图形模型描述这种因果关系理论。本教程将包括两个部分。 第一部分将介绍图形因果模型，作为表达因果假设的工具，并将干预作为形式化因果效应和反事实概念的操作。将给出使用此框架提出和回答公共卫生中实际因果问题的示例。第一部分将通过给出一种从观测研究中识别因果效应的通用算法进行总结，并说明如何才能根据这种效应对潜变量图形模型中所谓的截断后独立性约束进行解释。 第二部分将讨论潜在变量图形模型的递归分解（r分解），并说明如何通过这种分解来捕获在有向无环图（DAG）模型中未出现的截断后约束。将对此因果关系进行因果解释。第二部分将通过给出这样的模型的一些显着示例，包括在所有条件独立性上都一致的可测试的截然不同的模型，以及具有由单个截断后独立性完全指定的图的模型。 |
| 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; 图形模型; 条件独立性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-08：zyk |
| 最后编审: | 2021-05-08：zyk |
| 阅读次数: | 54 |