SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets

2026-05-15

AI Summary

This work proposes a novel method for learning nonlinear cyclic causal models from observational data containing unknown soft interventions, addressing key limitations of existing causal discovery approaches that typically assume acyclicity, Gaussian noise, and known intervention targets. By jointly inferring the causal graph structure and intervention targets through maximization of the log-likelihood, the method is the first to simultaneously handle cycles, soft interventions, and unknown intervention locations in a nonlinear setting. It leverages two normalizing flow architectures—compressive residual flows and neural spline flows—to flexibly model complex nonlinear causal mechanisms. Experimental results demonstrate that the proposed approach significantly outperforms current state-of-the-art methods on both synthetic and real-world datasets, achieving superior performance in both causal graph recovery and intervention target identification.

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.

Problem

• causal discovery
• cyclic systems
• soft interventions
• unknown intervention targets
• nonlinear models

Innovation

• cyclic causal discovery
• soft interventions
• unknown intervention targets
• nonlinear causal models
• normalizing flows