University of Cambridge > Talks.cam > Statistics > A machine learning approach for causal structure estimation in high dimensions

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

A machine learning approach for causal structure estimation in high dimensions

Download to your calendar using vCal

If you have a question about this talk, please contact Qingyuan Zhao .

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.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

G3, Bevan Braithwaite Building, TWI Ltd, Granta Park, Great Abington, Cambridge CB21 6AL Gatsby Room, Chanc Lewin Stroke & Rehabilitation Unit, Seminar Room, R2, Addenbrooke's Hospital

Other talks

Group Presentation 3 Tissue-scale communication in cancer development TOWARD AFFORDABLE PREVENTIVE HEALTHCARE: How basic science addresses grand challenges Title abstract tbc Talk title TBC CSAR lecture: In a post-Ozempic world, have we cured obesity?
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity