Our colleague Sindy Löwe will present her recent work at our AMLab Seminar and you are all cordially invited to this thrilling session on March 4th (Thursday) at 4:00 p.m. CET on Zoom. And then Sindy will give a talk titled “Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data”.
Title: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Abstract: Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information – for instance, the dynamics describing the effects of causal relations – which is lost when following this approach. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus makes use of the information that is shared. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under hidden confounding.
Paper Link: https://arxiv.org/abs/2006.10833
To gain more insight into Causal Discovery, feel free to join and discuss it! See you there 😊. See you there!