PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.
Optimizing data-driven causal discovery using knowledge- guided search
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
PICK adds a parent-finding subroutine for leaf nodes to speed up pruning in score-matching causal discovery, extending it from i.i.d. data to static and temporal network data.
citing papers explorer
-
PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.
-
Score-matching-based Structure Learning for Temporal Data on Networks
PICK adds a parent-finding subroutine for leaf nodes to speed up pruning in score-matching causal discovery, extending it from i.i.d. data to static and temporal network data.