Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism

TL;DR

This paper introduces a novel causal inference method leveraging latent instrumental variables as priors, reconciling independence of cause and mechanism approaches with traditional graphical models, and demonstrates its effectiveness on simulated and real data.

Contribution

It proposes a new algorithm that infers causal relationships between two variables using latent instrumental variables, bridging different causal inference paradigms.

Findings

The method is simple and highly accurate.

It outperforms state-of-the-art methods on benchmark data.

The approach effectively detects hidden causal structures.

Abstract

Causal inference methods based on conditional independence construct Markov equivalent graphs, and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations. In our contribution, we challenge to reconcile these two research directions. We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We show that the methods based on the independence of cause and mechanism, indirectly contain traces of the existence of the hidden instrumental variables. We derive a novel algorithm to infer causal relationships between two variables, and we validate the proposed method on simulated data and on a benchmark of cause-effect pairs. We illustrate by our experiments that the proposed approach is simple and extremely competitive in terms of empirical accuracy compared to the state-of-the-art methods.