Online Causal Inference Seminar

Free and open to the public
All seminars are on Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin).

Tuesday, January 18, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)

• Speaker: Sach Mukherjee (University of Cambridge)
• Title: A machine learning approach for causal structure estimation in high dimensions
• Abstract: Causal structure learning refers to the task of estimating graphical structures encoding causal relationships between variables. This remains challenging, especially under conditions of high dimensionality, latent variables and noisy, finite data, as seen in many real world applications. I will discuss our recent efforts to reframe specific aspects of causal structure learning from a machine learning perspective. The approaches I will discuss differ from classical structure learning tools in that rather than trying to establish a model of the data-generating process, they focus on minimizing a certain expected loss defined with respect to the causal structure of interest. The work is motivated by applications in high-dimensional molecular biology, and I will show empirical examples in which model-based predictions can be tested at large scale against experimental results.