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Artificial Intelligence

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Research Frontiers in Causal Inference in Machine Learning

Investigation of causal relationships and intervention effects in machine learning models to move beyond correlation-based predictions toward true causal understanding.

Causal Discovery from High-Dimensional Observational Data

Interventional Learning Beyond the Markov Assumption

Causal Representation Learning in Deep Neural Networks

Counterfactual Reasoning Across Distribution Shifts

Causal Inference in Temporal and Sequential Systems

Identifiability and Unconfoundedness in Complex Domains

Causal Abstraction and Multi-Scale Mechanistic Models

Learning Causal Graphs from Incomplete and Noisy Interventions

Fairness Through Causal Pathways and Mediation Analysis

Causal Discovery in Nonlinear Dynamical Systems

All Artificial Intelligence PhD categories