Speaker
Description
Feedback is pervasive in biological and biomedical systems, yet many causal discovery methods, including widely used score-based approaches such as NOTEARS, impose acyclicity and may therefore misrepresent gene regulatory, pharmacological, or cellular processes. Building on recent advances in cyclic causal inference, such as the intervention-capable Bicycle method, we investigate how graph-theoretic structure governs the feasibility and accuracy of causal discovery in directed networks with feedback.
We use biologically-inspired directed random graph models and tune their parameters to control key structural invariants, including spectral radius, cycle density, v-structure frequency, and degree heterogeneity. Using these networks as ground truth, we simulate data from linear and nonlinear structural equation models with controlled feedback characteristics. Causal structure is then inferred using both acyclicity-constrained (NOTEARS) and cyclic-capable (Bicycle) algorithms.
By regressing inference performance on the underlying graph invariants, we identify how specific topological features contribute to the recoverability of causal structure.
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