SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets

TL;DR

SCOUT is a novel framework for discovering nonlinear cyclic causal relationships from soft interventional data with unknown targets, outperforming existing methods in various settings.

Contribution

It introduces a new approach that handles nonlinear cyclic causality with unknown intervention targets using normalizing flows and likelihood maximization.

Findings

SCOUT outperforms state-of-the-art methods in causal graph recovery.

SCOUT effectively recovers unknown intervention targets.

The method works well on both synthetic and real-world data.

Abstract

Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely on the assumptions that (i) the underlying system is acyclic, (ii) the exogenous noise variables are Gaussian, and (iii) the intervention targets for the data-generating experiments are known. While these assumptions simplify the analysis, they are violated in real-life systems. Most existing methods that address these issues either assume the underlying model is linear or are constrained to operate in limited interventional settings. To that end, we propose SCOUT, a novel causal discovery framework for learning nonlinear cyclic causal relationships from soft interventional data with unknown targets. Our approach maximizes the data log-likelihood to recover the graph structure, using two normalizing-flow architectures: contractive residual flows and neural spline flows. Through experiments on synthetic and real-world data, we show that SCOUT outperforms state-of-the-art methods in both causal graph recovery and unknown target recovery across various interventional and noise settings.