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Statistical Laboratory

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.

Frontpage talks

Probability

Statistics

Cambridge Statistics Clinic

Statistics

13
Jul
Cambridge Statistics Clinic

Further information

Time:

03Jun
Jun 3rd 2022
14:00 to 15:00

Venue:

https://maths-cam-ac-uk.zoom.us/j/93998865836?pwd=VzVzN1VFQ0xjS3VDdlY0enBVckY5dz09

Speaker:

Sach Mukherjee (MRC Biostatistics Unit)

Series:

Statistics