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Data Driven Interdisciplinary Science

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Data Driven Interdisciplinary Science

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Research Frontiers in Causal Inference in Complex Systems

Develops statistical and computational methods to identify causal relationships in high-dimensional datasets from interconnected biological, physical, and social systems.

Causal Discovery in High-Dimensional Omics Data

Temporal Causal Networks in Dynamical Biological Systems

Causal Inference Across Multi-Modal Environmental Data

Counterfactual Reasoning in Climate-Socioeconomic Coupling

Causal Attribution in Emergent Social Media Phenomena

Backdoor Adjustment in Interconnected Infrastructure Systems

Causal Mechanisms Underlying Urban Health Disparities

Identifying Spurious Correlations in Financial Contagion Networks

Causal Pathways in Microbiome-Mediated Disease Progression

Intervention Effects in Nonlinear Neuroscience-Behavior Coupling

All Data-Driven Interdisciplinary Science PhD categories