Symbolic causal inference via operations on probabilistic circuits
Causal inference provides a means of translating a target causal query into a causal formula, which is a function of the observational distribution, given some assumptions on the domain. With the advent of modern neural probabilistic models, this opens up the possibility to perform accurate and trac...
| 主要な著者: | , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
OpenReview
2022
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