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Statistics Seminar

A machine learning approach for causal structure estimation in high dimensions

With Sach Mukherjee (MRC Biostatistics Unit)

A machine learning approach for causal structure estimation in high dimensions

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.

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