Algorithm

Philosophy

Causal inference

Exploit

Computer security

Theoretical computer science

Computer science

Graph

Mathematics

Inference

Epistemology

Econometrics

Artificial intelligence

Perspective (graphical)

Property (philosophy)

Directed acyclic graph

Machine learning

Adversarial system

Learning Directed Acyclic Graph (DAG) from purely observational data is a critical problem for causal inference. Most existing works tackle this problem by exploring gradient-based learning methods with a smooth characterization of acyclicity. A major shortcoming of current gradient based works is that they independently optimize SEMs with a single sample and neglect the interactions between different samples. In this paper, we consider DAG structure learning from the perspective of distributional optimization and design an adversarial framework named DAG-GAN to detect the DAG structure from data. We theoretically analyze the Nash equilibrium property of DAG-GAN and propose a novel score function to exploit the interactions between different samples. In addition, extensive experiments are conducted to validate the efficiency of DAG-GAN against several state-of-the-art DAG learning methods.

DAG-GAN: Causal Structure Learning with Generative Adversarial Nets