| dc.contributor.author | Beji, Céline | |
| dc.contributor.author | Benhamou, Éric | |
| dc.contributor.author | Bon, Michaël | |
| dc.contributor.author | Yger, Florian | |
| dc.contributor.author | Atif, Jamal | |
| dc.date.accessioned | 2021-01-15T09:27:21Z | |
| dc.date.available | 2021-01-15T09:27:21Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/21516 | |
| dc.language.iso | en | en |
| dc.subject | Machine Learning | en |
| dc.subject.ddc | 005 | en |
| dc.title | Estimating Individual Treatment Effects throughCausal Populations Identification | en |
| dc.type | Communication / Conférence | |
| dc.description.abstracten | Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one of both, is a challenging problems in causal learning. In this paper, we formulate this problem as an inference from hidden variables and enforce causal constraints based on a model of four exclusive causal populations. We propose a new version of the EM algorithm, coined as Expected-Causality-Maximization (ECM) algorithm and provide hints on its convergence under mild conditions. We compare our algorithm to baseline methods on synthetic and real-world data and discuss its performances. | en |
| dc.subject.ddclabel | Programmation, logiciels, organisation des données | en |
| dc.relation.conftitle | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020) | en |
| dc.relation.confdate | 2020-10 | |
| dc.relation.confcity | Brugges | en |
| dc.relation.confcountry | Belgium | en |
| dc.relation.forthcoming | non | en |
| dc.description.ssrncandidate | non | en |
| dc.description.halcandidate | oui | en |
| dc.description.readership | recherche | en |
| dc.description.audience | International | en |
| dc.relation.Isversionofjnlpeerreviewed | non | en |
| dc.relation.Isversionofjnlpeerreviewed | non | en |
| dc.date.updated | 2021-01-15T09:21:51Z | |
| hal.person.labIds | 989 | |
| hal.person.labIds | 989 | |
| hal.person.labIds | 115536 | |
| hal.person.labIds | 989 | |
| hal.person.labIds | 989 | |
| hal.identifier | hal-03111153 | * |
