Causal Effect Inference with Deep Latent-Variable Models. Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R. S., & Welling, M. In Proceedings of the 31st International Conference on Neural Information Processing Systems, of NIPS'17, 2017.
Causal Effect Inference with Deep Latent-Variable Models [pdf]Paper  abstract   bibtex   12 downloads  
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modelling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.
Downloads: 12
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021