Applications of Common Entropy for Causal Inference
Murat Kocaoglu, Sanjay Shakkottai, et al.
NeurIPS 2020
The use of a hypothetical generative model was been suggested for causal analysis of observa- tional data. The very assumption of a particular model is a commitment to a certain set of variables and therefore to a certain set of possible causes. Estimating the joint probability distribution of can be useful for predicting values of variables in view of the observed values of others, but it is not sufficient for inferring causal relationships. The model describes a single observable distribution and cannot a chain of effects of intervention that deviate from the observed distribution.
Murat Kocaoglu, Sanjay Shakkottai, et al.
NeurIPS 2020
Suchana Datta, Derek Greene, et al.
AICS 2020
Lu Cheng, Dmitriy A. Katz-Rogozhnikov, et al.
CHIL 2021
Lamogha Chiazor, Ngoc Lan Hoang, et al.
IJCAI 2023