Optimal transport for causal discovery

TL;DR

This paper introduces a novel optimal transport framework for causal discovery that leverages a dynamical-system perspective, improving robustness and achieving state-of-the-art results on benchmark datasets.

Contribution

It establishes a connection between FCMs and optimal transport, and develops a new algorithm for causal discovery based on this insight, extending to post-nonlinear models.

Findings

Achieves state-of-the-art results on synthetic datasets.

Demonstrates robustness to model assumptions.

Provides a new dynamical interpretation of causal discovery.

Abstract

To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the performance is sensitive to the model assumptions, which makes it difficult to use. In this paper, we provide a novel dynamical-system view of FCMs and propose a new framework for identifying causal direction in the bivariate case. We first show the connection between FCMs and optimal transport, and then study optimal transport under the constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of optimal transport under the FCM constraints, we determine the corresponding underlying dynamical process of the static cause-effect pair data. It provides a new dimension for describing static causal discovery tasks while enjoying more freedom for modeling the quantitative causal influences. In particular, we show that Additive Noise Models (ANMs) correspond to volume-preserving pressureless flows. Consequently, based on their velocity field divergence, we introduce a criterion for determining causal direction. With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-nonlinear models. Our method demonstrated state-of-the-art results on both synthetic and causal discovery benchmark datasets.