Causal regularization

Ernst Wit (Università della Svizzera italiana)

Friday 19th March 15:00-16:00 ZOOM:


Causality is the holy grail of science, but for millennia humankind has struggled to operationalize it efficiently. In recent decades, a number of more successful ways of dealing with causality in practice, such as propensity score matching, the PC algorithm and invariant causal prediction, have been introduced. However, approaches that use a graphical model formulation tend to struggle with the computational complexity, whenever the system gets large. Finding the causal structure typically becomes a combinatorial-hard problem.

In our causal inference approach, we build forth on ideas present in invariant causal prediction and the causal Dantzig, by replacing the combinatorial optimization by a continuous optimization using a form of causal regularization. This makes our method applicable to large systems. Furthermore, our method allows a precise formulation of the trade-off between in-sample and out-of-sample prediction error.

This is joint work with Lucas Kania. For an introduction on causal modelling:

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