Performance comparison of auto-tuned causal inference model architectures

Organisatsiooni nimi
Wise Plc
Kokkuvõte
The library for causal inference AutoML, https://github.com/transferwise/auto-causality, has now integrated models from the EconML library that use the Double Machine Learning, Doubly Robust Learning, and OrthoForest methods, for both unconfounded CATE and instrumental variable problems. This thesis would integrate the DeepIV model from EconML as well as several other neural network approaches from the literature, and compare their performance, including hyperparameter tuning, to those of the already available models, using benchmark datasets. As an optional extension, automated neural architecture search could be used to further refine those neural network approaches.
Lõputöö kaitsmise aasta
2022-2023
Juhendaja
Egor Kraev (Wise), Prof. Radwa El Shawi
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Magister
Märksõnad

Kandideerimise kontakt

 
Nimi
Egor Kraev
Tel
E-mail
egor.kraev@wise.com