Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

TL;DR

This paper introduces Amortized Causal Discovery, a novel framework that leverages shared dynamics in time-series data to infer causal graphs more effectively across diverse samples, outperforming traditional methods.

Contribution

It proposes a variational amortized model that captures shared causal dynamics, enabling efficient inference across different underlying causal graphs in time-series data.

Findings

Significant improvements in causal discovery accuracy.

Robustness to added noise.

Effective handling of hidden confounding.

Abstract

On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information which is lost when following this approach. Specifically, different samples may share the dynamics which describe the effects of their causal relations. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus leverages the shared dynamics information. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under added noise and hidden confounding.