Abstract: Many scientific questions are fundamentally causal in nature. Yet, existing causal inference methods cannot easily handle complex, high-dimensional data. Causal representation learning (CRL) seeks to fill this gap by embedding causal models in the latent space of a machine learning model. In this talk, I will provide an overview of our previous work on the theoretical and algorithmic foundations of CRL across a variety of settings. I will then present ongoing work on leveraging CRL methods for problems in computational biology, specifically for predicting the effects of unseen drug or gene perturbations from omics measurements. CRL requires rich experimental data, and single-cell biology offers unique opportunities for gaining new scientific insights by leveraging such methods. I will end by outlining my future research agenda aiming to leverage synergies between causal inference, machine learning, and computational biology.

Host: Prof. Wit Ernst-Jan Camiel