Causal Regularization
:
Ernst C. Wit
Università della Svizzera italiana
In presenza: stanza 24 (CU002)
Webinar: Webinar^.https://uniroma1.zoom.us/j/86881977368?pwd=SWRFc
VFjMDZTa0lXZk05TE1zNm5adz09
PassCode: 432940
Abstract:
Causality is the holy grail of science, but humankind has struggled to operationalize it for millennia. 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 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 and anchor regression, by replacing combinatorial optimization with a continuous optimization using a form of causal regularization.
This makes our method applicable to large systems.
Furthermore, our approach allows a precise formulation of the trade-off between in-sample and out-of-sample prediction error.
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Relatore:
Ernst Wit Università Svizzera italiana
Data:
17/03/2023 - 12:00
Luogo:
[In presenza: stanza 24 (CU002) Webinar: Webinar^.https://uniroma1.zoom.us/j/86881977368?pwd=SWRFc VFjMDZTa0lXZk05TE1zNm5adz09 PassCode: 432940 ]
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