Identifiability of Causal Models and Applications to Perturb-Seq Data

Identifiability of Causal Models and Applications to Perturb-Seq Data

Explore the concept of identifiability in causal models and its applications to Perturb-seq data in this comprehensive lecture by Chandler Squires from Valence Labs. Delve into structural causal models, interventions, and learning techniques for unknown-target interventions and unobserved variables. Examine latent factor causal models, causal disentanglement models, and linear causal disentanglement via intervention. Gain insights into ongoing work in the field and participate in a Q&A session. Discover how these advanced concepts are applied to learning gene regulatory networks from Perturb-seq data and predicting the effects of novel perturbations.