English
Treatment effect estimation with missing attributes
Virtualconference
Auteurs : Josse, Julie (Auteur de la Conférence)
CIRM (Editeur )
62H12 - Multivariate estimation
62N99 - None of the above but in this section
62P10 - Applications of statistics to biology and medical sciences
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/Josse.pdf
CIRM (Editeur )
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Résumé :
Inferring causal effects of a treatment or policy from observational data is central to many applications. However, state-of-the-art methods for causal inference suffer when covariates have missing values, which is ubiquitous in application.
Missing data greatly complicate causal analyses as they either require strong assumptions about the missing data generating mechanism or an adapted unconfoundedness hypothesis. In this talk, I will first provide a classification of existing methods according to the main underlying assumptions, which are based either on variants of the classical unconfoundedness assumption or relying on assumptions about the mechanism that generates the missing values. Then, I will present two recent contributions on this topic: (1) an extension of doubly robust estimators that allows handling of missing attributes, and (2) an approach to causal inference based on variational autoencoders adapted to incomplete data.
I will illustrate the topic an an observational medical database which has heterogeneous data and a multilevel structure to assess the impact of the administration of a treatment on survival.
Keywords : Causal inference; missing values; average treatment effect; double robust methods; generalized random forest; latent variables models
Codes MSC : Missing data greatly complicate causal analyses as they either require strong assumptions about the missing data generating mechanism or an adapted unconfoundedness hypothesis. In this talk, I will first provide a classification of existing methods according to the main underlying assumptions, which are based either on variants of the classical unconfoundedness assumption or relying on assumptions about the mechanism that generates the missing values. Then, I will present two recent contributions on this topic: (1) an extension of doubly robust estimators that allows handling of missing attributes, and (2) an approach to causal inference based on variational autoencoders adapted to incomplete data.
I will illustrate the topic an an observational medical database which has heterogeneous data and a multilevel structure to assess the impact of the administration of a treatment on survival.
Keywords : Causal inference; missing values; average treatment effect; double robust methods; generalized random forest; latent variables models
62H12 - Multivariate estimation
62N99 - None of the above but in this section
62P10 - Applications of statistics to biology and medical sciences
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/Josse.pdf
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 15/06/2020 Date de captation : 08/06/2020 Sous collection : Research talks arXiv category : Statistics Theory Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 00:29:00 Audience : Researchers Download : https://videos.cirm-math.fr/2020-06-04_Josse.mp4 
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Informations sur la Rencontre
Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2Organisateurs de la rencontre : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne Dates : 15/06/2020 - 19/06/2020 Année de la rencontre : 2020 URL Congrès : https://www.cirm-math.com/cirm-virtual-...
Données de citation
DOI : 10.24350/CIRM.V.19641503Citer cette vidéo: Josse, Julie (2020). Treatment effect estimation with missing attributes. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19641503 URI : http://dx.doi.org/10.24350/CIRM.V.19641503 |
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Bibliographie
- MAYER, Imke, JOSSE, Julie, RAIMUNDO, Félix, et al. MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models. arXiv preprint arXiv:2002.10837, 2020. - https://arxiv.org/abs/2002.10837
- MAYER, Imke, WAGER, Stefan, GAUSS, Tobias, et al. Doubly robust treatment effect estimation with missing attributes. arXiv preprint arXiv:1910.10624, 2019. - https://arxiv.org/abs/1910.10624
- JOSSE, Julie, PROST, Nicolas, SCORNET, Erwan, et al. On the consistency of supervised learning with missing values. arXiv preprint arXiv:1902.06931, 2019. - https://arxiv.org/abs/1902.06931
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