SCOUT recovers nonlinear cyclic causal graphs and unknown soft intervention targets from interventional data using contractive residual flows and neural spline flows to maximize log-likelihood.
Discovering Cyclic Causal Models by Independent Components Analysis
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abstract
We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM's graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is 'stable'.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
SCOUT recovers nonlinear cyclic causal graphs and unknown soft intervention targets from interventional data using contractive residual flows and neural spline flows to maximize log-likelihood.