ioanabica/Invariant-Causal-Imitation-Learning

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Created over 2 years ago
Updated over 2 years ago

ioanabica/Invariant-Causal-Imitation-Learning

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Code for NeurIPS 2021 paper: "Invariant Causal Imitation Learning for Generalizable Policies" by I. Bica, D. Jarrett, M. van der Schaar

Time-Series-Deconfounder

Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. Bica, A. M. Alaa, M. van der Schaar

Counterfactual-Recurrent-Network

Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. Bica, A. M. Alaa, J. Jordon, M. van der Schaar

SCIGAN

Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bica, J. Jordon, M. van der Schaar

DiffVAE

Code for Nature Scientific Reports 2020 paper: "Unsupervised generative and graph neural methods for modelling cell differentiation" by Ioana Bica, Helena Andrés-Terré, Ana Cvejic, Pietro Liò

Jupyter Notebook

HTCE-learners

Code for NeurIPS 2022 paper: "Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation" by I. Bica, M. van der Schaar

COVID-19-Brazil

Code for Lancet Global Health 2020 paper: "Ethnic and regional variation in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study" by Pedro Baqui, Ioana Bica, Valerio Marra, Ari Ercole and Mihaela van der Schaar

Jupyter Notebook

VAE-GeneExpression

Unsupervised neural methods for modelling cell differentiation

Multi-Omics-Data-Integration

Multi-omics data integration using cross-modal neural networks