An Improved Neural Network Model for Treatment Effect Estimation - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

An Improved Neural Network Model for Treatment Effect Estimation

Niki Kiriakidou
  • Fonction : Auteur
  • PersonId : 1406711
Christos Diou
  • Fonction : Auteur
  • PersonId : 1406712

Résumé

Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes for leveraging this data. In this work, we propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture. The proposed model exploits the covariates as well as the outcomes of neighboring instances in training data. Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.
Fichier sous embargo
Fichier sous embargo
0 4 6
Année Mois Jours
Avant la publication
mercredi 1 janvier 2025
Fichier sous embargo
mercredi 1 janvier 2025
Connectez-vous pour demander l'accès au fichier

Dates et versions

hal-04668639 , version 1 (07-08-2024)

Licence

Identifiants

Citer

Niki Kiriakidou, Christos Diou. An Improved Neural Network Model for Treatment Effect Estimation. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.147-158, ⟨10.1007/978-3-031-08337-2_13⟩. ⟨hal-04668639⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More