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Treatment effect estimation with missing attributes
Virtualconference
Authors : ... (Author of the conference)
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62H12 - Multivariate estimation
62N99 - None of the above but in this section
62P10 - Applications of statistics to biology and medical sciences
Additional resources :
https://www.cirm-math.fr/RepOrga/2146/Slides/Josse.pdf
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Abstract :
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
MSC Codes : 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
Additional resources :
https://www.cirm-math.fr/RepOrga/2146/Slides/Josse.pdf
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Information on the Video
Language : EnglishAvailable date : 15/06/2020 Conference Date : 08/06/2020 Subseries : Research talks arXiv category : Statistics Theory Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 00:29:00 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-06-04_Josse.mp4 
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Information on the Event
Event Title : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2Dates : 15/06/2020 - 19/06/2020 Event Year : 2020 Event URL : https://www.cirm-math.com/cirm-virtual-...
Citation Data
DOI : 10.24350/CIRM.V.19641503Cite this video as: (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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Bibliography
- 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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