Identifying Causal Direction via Variational Bayesian Compression

TL;DR

This paper introduces a novel method for causal inference that uses variational Bayesian neural networks to estimate codelengths, improving accuracy and computational efficiency over previous Gaussian process-based approaches.

Contribution

It proposes a new approach leveraging variational Bayesian neural networks for causal direction identification, addressing limitations of prior methods.

Findings

Effective on synthetic and real-world datasets

Outperforms most related methods in experiments

Balances model fitness and computational complexity

Abstract

Telling apart the cause and effect between two random variables with purely observational data is a challenging problem that finds applications in various scientific disciplines. A key principle utilized in this task is the algorithmic Markov condition, which postulates that the joint distribution, when factorized according to the causal direction, yields a more succinct codelength compared to the anti-causal direction. Previous approaches approximate these codelengths by relying on simple functions or Gaussian processes (GPs) with easily evaluable complexity, compromising between model fitness and computational complexity. To address these limitations, we propose leveraging the variational Bayesian learning of neural networks as an interpretation of the codelengths. This allows the improvement of model fitness, while maintaining the succinctness of the codelengths, and the avoidance of the significant computational complexity of the GP-based approaches. Extensive experiments on both synthetic and real-world benchmarks in cause-effect identification demonstrate the effectiveness of our proposed method, showing promising performance enhancements on several datasets in comparison to most related methods.