Partial Identifiability

Partial identifiability addresses the challenge of uniquely recovering underlying model parameters or causal relationships from observed data, a crucial issue in various machine learning and causal inference tasks. Current research focuses on developing theoretical guarantees for identifiability in diverse models, including variational autoencoders, graph neural networks, and mixture models, often leveraging techniques like sparsity constraints, auxiliary data, or specific structural assumptions to achieve partial or conditional identifiability. Overcoming this limitation is vital for ensuring reliable model interpretation and accurate causal inference, impacting fields ranging from genomics and drug discovery to recommendation systems and policy evaluation. The development of robust algorithms and theoretical frameworks for handling partial identifiability is a key area of ongoing investigation.