Parameter Identifiability

Parameter identifiability addresses the crucial question of whether a model's parameters can be uniquely determined from available data. Current research focuses on establishing identifiability conditions for various models, including linear causal models (with and without latent variables), stochastic differential equations, and machine learning architectures like neural networks, often employing graphical methods and algebraic geometry techniques. This research is vital for ensuring the reliability and interpretability of models across diverse fields, from causal inference and dynamical systems modeling to unsupervised learning and ranking problems, ultimately improving the accuracy and trustworthiness of scientific findings and practical applications.