A Language for Counterfactual Generative Models
Zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama
2021年份
21被引次数
6顶会引用
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.
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引用它的顶会 Paper6
- Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal ReasoningWenhao Ding, Haohong Lin, Bo Li, Ding ZhaoNeurIPS 2022 · 被引用 59 次
- Probabilistic Reasoning Across the Causal HierarchyDuligur Ibeling, Thomas IcardAAAI 2020 · 被引用 35 次
- A Topological Perspective on Causal InferenceDuligur Ibeling, Thomas IcardNeurIPS 2021 · 被引用 12 次
- Doubly Abductive Counterfactual Inference for Text-Based Image EditingXue Song, Jiequan Cui, Hanwang Zhang, Jingjing Chen 等CVPR 2024 · 被引用 7 次
- BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement LearningHaohong Lin, Wenhao Ding, Jian Chen, Laixi Shi 等NeurIPS 2024 · 被引用 5 次
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