Ioana Bica


I am a fourth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. My PhD research focuses on building machine learning methods for improving and understanding decision making. To achieve this, I have worked on developing causal inference methods capable of estimating the individualized effect of interventions (e.g. actions or treatments) from observational data. Causal inference enables us to perform “what if” (counterfactual) reasoning--Given the current history of observations, what would happen if we took a particular action or sequence of actions? Then, based on the estimated counterfactual outcomes, we can decide which intervention or sequence of interventions will result in the best outcome. In my work, I have built methods that can estimate such counterfactual outcomes for discrete actions taken over time, both in the presence and absence of hidden confounders, but also for continuous intervention in the static setting. Moreover, I have also been doing research at the intersection of causality, inverse reinforcement learning, and imitation learning to build machine learning methods for explaining sequential decision making on the basis of demonstrated behavior and to learn policies that are robust to distribution shifts in the deployed environment.

My research experience also includes an internship at DeepMind where I’ve been working with Jovana Mitrović on self-supervised learning and causality with the aim of learning better representations for objects in images.

Prior to my PhD, I have completed a Bachelor’s degree and a Master’s degree in Computer Science at the University of Cambridge where I have worked with Prof. Pietro Liò on multi-modal data integration and unsupervised learning for genomics data. During this time, I have also interned at Google four times.

Research interests

  • Causal Inference
  • Treatment Effects Estimation
  • Imitation Learning
  • Inverse Reinforcement Learning
  • Self-Supervised Learning
  • Machine Learning for Healthcare


Sep 27, 2021

Three papers accepted to NeurIPS 2021 on "Invariant Causal Imitation Learning for Generalizable Policies", "SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes" and "Time-series Generation by Contrastive Imitation".

June 21, 2021

Joined DeepMind as a Research Scientist intern where I'll be working with Jovana Mitrović on self-supervised learning and causality.

May 1, 2021

Joined the Machine Learning for Healthcare Symphosium organizing committee as the Outreach Chair.

April 1, 2021

Joined the Women in Machine Learning Board of Directors.

Dec 12, 2020

Our workshop on AI for Public Health was accepted to ICLR 2021.

Sep 6, 2020

Joining the organizing team for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020 to lead the Submission Mentorship Program.

Aug 17, 2020

Excited to be Teaching Assistant at the Oxford Machine Learning Summer School.

Apr 10, 2020
Apr 1, 2020

Organizing the ELLIS agains COVID-19 event series.

Dec 19, 2019

Our paper on "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" was selected as a spotlight presentation to ICLR 2020.