DeepAI AI Chat
Log In Sign Up

Amortized learning of neural causal representations

08/21/2020
by   Nan Rosemary Ke, et al.
5

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 learned knowledge to help with learning new causal models. In order to tackle these challenges, we represent a novel algorithm called causal relational networks (CRN) for learning causal models using neural networks. The CRN represent causal models using continuous representations and hence could scale much better with the number of variables. These models also take in previously learned information to facilitate learning of new causal models. Finally, we propose a decoding-based metric to evaluate causal models with continuous representations. We test our method on synthetic data achieving high accuracy and quick adaptation to previously unseen causal models.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/24/2018

Learning and Testing Causal Models with Interventions

We consider testing and learning problems on causal Bayesian networks as...
09/26/2013

A Sound and Complete Algorithm for Learning Causal Models from Relational Data

The PC algorithm learns maximally oriented causal Bayesian networks. How...
07/18/2022

A Meta-Reinforcement Learning Algorithm for Causal Discovery

Causal discovery is a major task with the utmost importance for machine ...
08/16/2021

WiseR: An end-to-end structure learning and deployment framework for causal graphical models

Structure learning offers an expressive, versatile and explainable appro...
05/20/2021

To do or not to do: finding causal relations in smart homes

Research in Cognitive Science suggests that humans understand and repres...
01/30/2019

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

We propose to meta-learn causal structures based on how fast a learner a...
10/14/2020

Learning Robust Models Using The Principle of Independent Causal Mechanisms

Standard supervised learning breaks down under data distribution shift. ...