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Muralikrishnna Guruswamy Sethuraman

2 accepted papers

2026

SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets

ICML 2026poster

Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most of the existing causal discovery algorithms rely on the assumptions that (i) the underlying system is acyclic, (ii) the exogenous noise variables are Gaussian, and…

Cited by 0SourceScholar
2025

Differentiable Cyclic Causal Discovery Under Unmeasured Confounders

NeurIPS 2025spotlight

Understanding causal relationships between variables is fundamental across scientific disciplines. Most causal discovery algorithms rely on two key assumptions: (i) all variables are observed, and (ii) the underlying causal graph is acyclic. While these assumptions simplify theoretical analysis, the…

Cited by 0SourceScholar