arXiv subjects

S. A. Adedayo

Publications and source records attributed to S. A. Adedayo.

2 recordsLinked to original sources

arXiv2026

Conditioning-Depth Diagnostics for Hidden Memory in Temporal Causal Discovery

Reliable causal discovery in timeseries requires conditioning sets that capture the system state. When predictive history is omitted, residual dependence can appear as direct causal links. We test state adequacy by measuring how inferred graphs change as conditioning depth increases while the reported causal lag stays fixed. Under an adequate finite-order Markov representation, graphs should stabilize once enough observed history is conditioned on; latent common drive, omitted lags, nonstationarity, and measurement dynamics can instead produce depth sensitivity. We formalize this idea with graph instability statistics and evaluate c-GC and c-GC*, the two learners whose depth parameter implements a matched fixed-horizon history intervention. PCMCI+ and JPCMCI+ are excluded from the primary comparison because adaptive parent selection makes nominal depth edge-specific. In paired simulations, a clean order-1 process was stable in every repeat, whereas an AR(1) latent common driver produced positive instability in all c-GC repeats and eight of ten c-GC* repeats. In calcium imaging recordings, connectivity drops at the first transition beyond the one-lag baseline and then levels off, but B=200 bootstrap calibration does not reject the fitted order-1 null. The workflow therefore flags hidden memory or observed-state inadequacy without identifying the generating mechanism or recovering a latent graph.

stat.AP↗

arXiv2026

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

Granger causality (GC) is widely used to infer directed relationships in time-series data. However, its predictive criterion does not by itself distinguish direct causal effects from dependencies induced by common causes, indirect paths, collider conditioning, or model misspecification. We revisit this limitation by interpreting bivariate and multivariate GC through causal Bayesian networks and Reichenbachs common cause principles. Under explicit graphical assumptions, bivariate GC provides a marginal dependence check, while multivariate GC tests whether the same association persists after conditioning on relevant histories. This view motivates causalised Granger causality (c-GC), which combines the two decisions, and c-GC*, a more conservative variant with a richer conditioning set. We validate both methods on synthetic dynamical systems, established time-series causal discovery benchmarks, Sachs protein-signalling data, and Lorenz-96 simulations. The results show that the proposed criteria recover plausible causal structure in settings with delayed effects, cycles, bidirectional links, and nonlinear or noisy dynamics. The framework clarifies how GC-style inference can be given a causal interpretation without treating temporal prediction alone as sufficient evidence of causation.

stat.ML↗