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Observational causal modeling. Analyzing the relationship between quality of life at work and firm profitability| old_uid | 18526 |
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| title | Observational causal modeling. Analyzing the relationship between quality of life at work and firm profitability |
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| start_date | 2020/11/13 |
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| schedule | 15h |
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| online | no |
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| details | Pour participer au séminaire, merci de vous inscrire sur Listsem https://listsem.ehess.fr/. Le code d'accès sera envoyé à tous les inscrits. |
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| summary | The increasing pressure toward transparent, explainable and accountable reasoning in Artificial Intelligence has caused renewed attention to be given to causal models.
While the royal road toward establishing causal relationships is based on randomized controlled experiments, these might be subject to severe ethic or feasibility restrictions, or too costly in some domains.
Observational causal modeling aims to exploit existing data (usually gathered for other purposes) to investigate the presence of causal relationships among variables.
We will present a new approach based on adversarial generative models – a class of machine learning models in which learning is done through the competion between two neural networks. We will illustrate this approach on a use case, exploiting data provided by SECAFI (a society approved by the French Ministry of Labour for occupational health missions).
Joint work: Diviyan Kalainathan, Olivier Goudet, David Lopez-Paz, Isabelle Guyon. |
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| responsibles | <not specified> |
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