12 citations · 16 across the 5 of their papers we have counts for
5 papers
Causal Abstraction Inference under Lossy Representations
Kevin Xia, Elias Bareinboim
The study of causal abstractions bridges two integral components of human intelligence: the ability to determine cause and effect, and the ability to interpret complex patterns int…
Neural Causal Abstractions
Kevin Xia, Elias Bareinboim
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…
Neural Causal Models for Counterfactual Identification and Estimation
Kevin Xia, Yushu Pan, Elias Bareinboim
Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterf…
Causal Transportability for Visual Recognition
Chengzhi Mao, Kevin Xia, James Wang +4
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poo…
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio +1
One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mechanisms and exogenous sources of random v…