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Appears in collection : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

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.

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Citation data

  • DOI 10.24350/CIRM.V.19641503
  • Cite this video Josse Julie (6/8/20). Treatment effect estimation with missing attributes. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19641503
  • URL https://dx.doi.org/10.24350/CIRM.V.19641503



  • 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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