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Research Frontiers in Causal Inference in Complex Systems

Development of methods to identify and quantify causal relationships in high-dimensional, non-linear, and temporal data systems.

Causal Graphs in High-Dimensional Omics Data
Temporal Causal Discovery in Non-Stationary Networks
Causal Inference Across Heterogeneous Data Modalities
Unmixing Confounding in Observational Social Systems
Causal Mechanisms in Nested Hierarchical Structures
Interventional Robustness Under Model Misspecification
Causal Pathways in High-Frequency Financial Markets
Latent Confounders and Identifiability in Black-Box Systems
Causal Reasoning in Feedback-Loop Dominated Ecosystems
Transfer Learning of Causal Structures Across Domains

All Data Science PhD categories