Estimating Individual Treatment Effects through Causal Populations Identification

• URL: http://arxiv.org/abs/2004.05013v3
• Date: Wed, 6 May 2020 11:12:37 GMT
• Title: Estimating Individual Treatment Effects through Causal Populations Identification
• Authors: C\'eline Beji, Micha\"el Bon, Florian Yger, Jamal Atif
• Abstract summary: 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.
• Score: 11.936520478641182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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