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Invariance and Causality

May 12 @ 11:00 am

Why are we interested in the causal structure of a process? In classical prediction tasks, for example, it seems that no causal knowledge is required. In many situations, however, we are interested in a system’s behavior after parts of this system have been changed. Here, causal models become important because they are usually considered invariant under those changes. A causal prediction (which uses only direct causes of the target variable as predictors) remains valid even if we intervene on predictor variables or change the whole experimental setting. We show how we can use invariance in order to estimate the causal structure. This talk does not require any knowledge about causal concepts.

Details

Date:
May 12
Time:
11:00 am
Event Category:
Website:
https://stat.mit.edu/events/jonas-peters/

Other

Bldg-Room #
E18-304