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Showing 1 to 3 of 3 for “"causal representation learning"”.

  1. Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment

    This thesis brings together ideas from causality and representation learning. Causal models provide rich descriptions of complex systems as sets of mechanisms by which each variable is influenced by its direct causes. They support reasoning about manipulating parts of the system, capture a whole …

    cambridge Repository record for Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment (opens in a new tab)

  2. Causal Representation Learning for Predicting Genetic Perturbation Effects on Single Cells

    … expression profiles. While existing deep learning approaches excel at interpolating within observational data, they often struggle to extrapolate to novel perturbations. To address this limitation, this study introduces a hybrid framework that integrates a linear causal model, grounded in …

    mit Repository record for Causal Representation Learning for Predicting Genetic Perturbation Effects on Single Cells (opens in a new tab)

  3. Causal Foundations for Pragmatic Data Science

    … decision-making, emphasizing the importance of causal models in science, i.e., models which describe the possible effects of actions upon a system. The work contained explores central topics in this domain, including causal discovery (learning causal models from data), causal representation

    mit Repository record for Causal Foundations for Pragmatic Data Science (opens in a new tab)