图 书 馆
动态因果网络中的可识别性和可传输性Identifiability and Transportability in Dynamic Causal Networks |
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| 课程网址: | https://videolectures.net/videos/kdd2016_blondel_causal_networks |
| 主讲教师: | Gilles Blondel |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2016-10-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for the identification of causal effects in Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment given a Dynamic Causal Network and probability distributions of passive observations of its variables, whenever possible. We note the existence of two types of hidden confounder variables that affect in substantially different ways the identification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the result of causal experiments in a source domain may be used for the identi- fication of causal effects in a target domain. |
| 关 键 词: | 动态因果网络; 贝叶斯网络; 可识别性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-01-07：liyq |
| 最后编审: | 2025-01-07：liyq |
| 阅读次数: | 20 |