Amortized learning of neural causal representations

Nan Rosemary Ke
Nan Rosemary Ke
Jane. X. Wang
Jane. X. Wang
Jovana Mitrovic
Jovana Mitrovic
Martin Szummer
Martin Szummer
Cited by: 0|Bibtex|Views9
Other Links: arxiv.org

Abstract:

Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot leverage previously ...More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments