SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
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 ...
| Authors | Alpar Turkoglu, Muralikrishnna G. Sethuraman, Faramarz Fekri |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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