Corpus ID: 211296381

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models

@article{Mayer2020MissDeepCausalCI,
  title={MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models},
  author={Imke Mayer and Julie Josse and F. M. Raimundo and Jean-Philippe Vert},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.10837}
}
  • Imke Mayer, Julie Josse, +1 author Jean-Philippe Vert
  • Published in ArXiv 2020
  • Computer Science, Mathematics
  • Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications. However, state-of-the-art methods for causal inference seldom consider the possibility that covariates have missing values, which is ubiquitous in many real-world analyses. Missing data greatly complicate causal inference procedures as they require an adapted unconfoundedness hypothesis which can be difficult to justify in practice. We circumvent this issue by considering… CONTINUE READING

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