DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks

04/01/2022
by   Hristo Petkov, et al.
0

The combinatorial search space presents a significant challenge to learning causality from data. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint, allowing for the exploration of deep generative models to better capture data sample distributions and support the discovery of Directed Acyclic Graphs (DAGs) that faithfully represent the underlying data distribution. However, so far no study has investigated the use of Wasserstein distance for causal structure learning via generative models. This paper proposes a new model named DAG-WGAN, which combines the Wasserstein-based adversarial loss, an auto-encoder architecture together with an acyclicity constraint. DAG-WGAN simultaneously learns causal structures and improves its data generation capability by leveraging the strength from the Wasserstein distance metric. Compared with other models, it scales well and handles both continuous and discrete data. Our experiments have evaluated DAG-WGAN against the state-of-the-art and demonstrated its good performance.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

06/03/2022

Causality Learning With Wasserstein Generative Adversarial Networks

Conventional methods for causal structure learning from data face signif...
02/25/2019

Wasserstein-Wasserstein Auto-Encoders

To address the challenges in learning deep generative models (e.g.,the b...
06/13/2022

Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs

Auto-encoder models that preserve similarities in the data are a popular...
05/14/2019

Learning Generative Models across Incomparable Spaces

Generative Adversarial Networks have shown remarkable success in learnin...
04/10/2019

Sliced Wasserstein Generative Models

In generative modeling, the Wasserstein distance (WD) has emerged as a u...
06/18/2018

Banach Wasserstein GAN

Wasserstein Generative Adversarial Networks (WGANs) can be used to gener...
03/25/2022

Amortized Projection Optimization for Sliced Wasserstein Generative Models

Seeking informative projecting directions has been an important task in ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.