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Constraint-based causal Discovery from NOnstationary/heterogeneous Data (CD-NOD)

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It is commonplace to encounter nonstationary or heterogeneous data, of which the under- lying generating process changes over time or across data sets. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this talk, I will present a principled framework for causal discovery from such data, called Constraint-based causal Discovery from NOnstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, I will introduce an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, I will present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, I will discuss how to efficiently estimate the “driving force” of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional and interpretable representation of the main components of the changes. Finally, I will show that nonstationarity also benefits causal structure identification with stationary confounders.

This talk is part of the Machine Learning @ CUED series.

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