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Found 4 papers, 3 papers with code
Neural Causal Abstractions
The abilities of humans to understand the world in terms of cause and effect relationships, as well as to compress information into abstract concepts, are two hallmark features of human intelligence.
Neural Causal Models for Counterfactual Identification and Estimation
We show that this algorithm is sound and complete for deciding counterfactual identification in general settings.
Causal Transportability for Visual Recognition
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM.
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