ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Causal Inference and Treatment Effects


  1. A. M. Alaa, M. van der Schaar, "Validating Causal Inference Models via Influence Functions," International Conference on Machine Learning (ICML), 2019. [Link]
  2. I. Bica, A. M. Alaa, and M. van der Schaar, "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders," Submitted 2019. [Link]
  3. C. Lee, N. Mastronarde, and M. van der Schaar, "Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  4. O. Atan, W. R. Zame, M. van der Schaar, "Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features," Machine Learning, 2018. [Link]
  5. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  6. O. Atan, W. R. Zame, M. van der Schaar, "Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks," ICML 2018 Causal Machine Learning Workshop, 2018. [Link]
  7. A. M. Alaa, M. van der Schaar, "Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms," IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2018. [Link]
  8. A. M. Alaa, M. van der Schaar, "Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design," ICML, 2018. [Link]
  9. J. Yoon, J. Jordon, M. van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets," ICLR, 2018. [Link]
  10. O. Atan, J. Jordon, M. van der Schaar, "Deep-Treat: Learning Optimal Personalized Treatments from Observational Data using Neural Networks," AAAI, 2018. [Link]
  11. A. M. Alaa, M. van der Schaar, "Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes," NIPS, 2017. [Link] [Supplementary Materials]
  12. A. M. Alaa, M. Weisz, M. van der Schaar, "Deep Counterfactual Networks with Propensity-Dropout," ICML 2017 - Workshop on Principled Approaches to Deep Learning., 2017. [Link]
  13. W. Whoiles, M. van der Schaar, "Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design," ICML, 2016. [Link]