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Estimating Individual Treatment Effects throughCausal Populations Identification

Beji, Céline; Benhamou, Éric; Bon, Michaël; Yger, Florian; Atif, Jamal (2020), Estimating Individual Treatment Effects throughCausal Populations Identification, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), 2020-10, Brugges, Belgium

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Type
Communication / Conférence
Date
2020
Conference title
28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)
Conference date
2020-10
Conference city
Brugges
Conference country
Belgium
Metadata
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Author(s)
Beji, Céline
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Benhamou, Éric
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Bon, Michaël
autre
Yger, Florian cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Atif, Jamal
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
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.
Subjects / Keywords
Machine Learning