ICML 2025

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience:
Leveraging Generative Factor Models of Observed Time Series
Zachary C. Brown 1 David Carlson 1 2
Abstract
The field of hypothesis generation promises to re-
duce costs in neuroscience by narrowing the range
of interventional studies needed to study various
phenomena. Existing machine learning methods
can generate scientific hypotheses from complex
datasets, but many approaches assume causal rela-
tionships are static over time, limiting their appli-
cability to systems with dynamic, state-dependent
behavior, such as the brain. While some tech-
niques attempt dynamic causal discovery through
factor models, they often restrict relationships to
linear patterns or impose other simplifying as-
sumptions. We propose a novel method that mod-
els dynamic graphs as a conditionally weighted
superposition of static graphs, where each static
graph can capture nonlinear relationships. This
approach enables the detection of complex, time-
varying interactions between variables beyond lin-
ear limitations. Our method improves f1-scores
of predicted dynamic causal patterns by roughly
22-28% on average over baselines in some of our
experiments, with some improvements reaching
well over 60%. A case study on real brain data
demonstrates our method’s ability to uncover re-
lationships linked to specific behavioral states,
offering valuable insights into neural dynamics.
1. Introduction
Causal discovery methods applied to brain dynamics hold
great promise for designing targeted interventions in var-
ious neurological diseases. A typical approach is to gen-
erate hypotheses about causal relationships between brain
1Department of Electrical and Computer Engineering, Duke
University, Durham, NC 15213, USA 2Departments of Civil and
Environmental Engineering; Biostatistics and Bioinformatics; and
Computer Science, Duke University, Durham, NC 15213, USA.
Correspondence to: Zachary C. Brown <zac.brown@duke.edu>,
David Carlson <dav...