Medianovskyi, K., Malakauskas, A., Lakstutiene, A., Ben Yahia, S.

prindi

Interpretable machine learning for heterogeneous treatment effect estimators with Double ML: a case of access to credit for SMEs

autor

Medianovskyi, Kyrylo

Malakauskas, Aidas

Lakstutiene, Ausrine

Ben Yahia, Sadok

vastutusandmed

Kyrylo Medianovskyi, Aidas Malakauskas, Ausrine Lakstutiene, Sadok Ben Yahia

allikas

Procedia computer science

kirjastus/väljaandja

Elsevier

ajakirja aastakäik number kuu

vol. 225

ilmumisaasta

2023

leheküljed

p. 2163-2172 : ill

konverentsi nimetus, aeg

27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023, 6-8 September 2023

konverentsi toimumispaik

Athens, Greece

leitav

https://doi.org/10.1016/j.procs.2023.10.207

märksõna

tehisintellekt

tehisõpe

võtmesõna

CATE

double ML

explainable artificial intelligence

interpretable machine learning

Partial dependence plot

SHAP

ISSN

1877-0509

märkused

Bibliogr.: 38 ref

The special issue 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023, Athens, 6 September 2023 - 8 September 2023

Open Access

teaduspublikatsioon

klassifikaator

3.1

Scopus

Conference proceeding at Scopus

Article at Scopus

kategooria (üld)

Computer science

kategooria (alam)

Computer science. General computer science

kvartiil

TTÜ struktuuriüksus

tarkvarateaduse instituut

keel

inglise

Medianovskyi, K., Malakauskas, A., Lakstutiene, A., Ben Yahia, S. Interpretable machine learning for heterogeneous treatment effect estimators with Double ML: a case of access to credit for SMEs // Procedia computer science (2023) vol. 225, p. 2163-2172 : ill. https://doi.org/10.1016/j.procs.2023.10.207