A Language for Counterfactual Generative Models

被引：0

作者：

Tavares, Zenna ^{[1
]}

Koppel, James ^{[1
]}

Zhang, Xin ^{[2
]}

Das, Ria ^{[1
]}

Solar-Lezama, Armando ^{[1
]}

机构：

[1] MIT, CSAIL, Cambridge, MA 02139 USA

[2] Peking Univ, Dept Comp Sci & Technol, Key Lab High Confidence Software Technol, Minist Educ, Beijing, Peoples R China

来源：

INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 | 2021年 / 139卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present OMEGA(C), a probabilistic programming language with support for counterfactual inference. Counterfactual inference means to observe some fact in the present, and infer what would have happened had some past intervention been taken, e.g. "given that medication was not effective at dose x, what is the probability that it would have been effective at dose 2x?" We accomplish this by introducing a new operator to probabilistic programming akin to Pearl's do, define its formal semantics, provide an implementation, and demonstrate its utility through examples in a variety of simulation models.

引用

页码：7180 / 7191

页数：12

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