van der Schaar Lab

Note : For the most recent papers and code, checkout https://github.com/vanderschaarlab.

Legacy code : This repository contains the implementations of algorithms developed by the van der Schaar Lab for papers before 2023.

Content

An overview of the content of this repository is as below:

.
├── alg/        # Directory contains algorithms.
├── app/        # Directory contains apps.
├── cfg/        # Directory contains common config.
├── doc/        # Directory contains common docs.
├── init/       # Directory contains algorithms.
├── template/   # Directory contains templates.
└── util/       # Directory contains common utilities.

Publications

The publications and the corresponding locations in the repo are listed below:

Paper [Link]	Journal/Conference	Code
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes [Link]	NIPS 2017	alg/causal_multitask_gaussian_processes_ite
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks [Link]	NIPS 2017	alg/dgp_survival
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning [Link]	ICML 2018	alg/autoprognosis
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design [Link]	ICML 2018	alg/causal_multitask_gaussian_processes_ite
GAIN: Missing Data Imputation using Generative Adversarial Nets [Link]	ICML 2018	alg/gain
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks [Link]	ICML 2018	alg/RadialGAN
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets [Link]	ICLR 2018	alg/ganite
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks [Link]	ICLR 2018	alg/DeepSensing (MRNN)
DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks [Link]	AAAI 2018	alg/deephit
INVASE: Instance-wise Variable Selection using Neural Networks [Link]	ICLR 2019	alg/invase
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees [Link]	ICLR 2019	alg/pategan
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks [Link]	ICLR 2019	alg/knockoffgan
ASAC: Active Sensing using Actor-Critic Models [Link]	MLHC 2019	alg/asac
Demystifying Black-box Models with Symbolic Metamodels [Link]	NeurIPS 2019	alg/symbolic_metamodeling
Differentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate [Link]	NeurIPS 2019	alg/dpbag
Time-series Generative Adversarial Networks [Link]	NeurIPS 2019	alg/timegan
Attentive State-Space Modeling of Disease Progression [Link]	NeurIPS 2019	alg/attentivess
Conditional Independence Testing using Generative Adversarial Networks [Link]	NeurIPS 2019	alg/gcit
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks based on Longitudinal Data [Link]	IEEE	alg/dynamic_deephit
Temporal Quilting for Survival Analysis [Link]	AISTATS 2019	alg/survivalquilts
Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations [Link]	ICLR 2020	alg/counterfactual_recurrent_network
Contextual Constrained Learning for Dose-Finding Clinical Trials [Link]	AISTATS 2020	alg/c3t_budgets
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects [Link]	AISTATS 2020	alg/dklite
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [Link]	AISTATS 2020	alg/dynamic_disease_network_ddp
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [Link]	AISTATS 2020	alg/smsdkl
Temporal Phenotyping using Deep Predicting Clustering of Disease Progression [Link]	ICML 2020	alg/ac_tpc
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders [Link]	ICML 2020	alg/time_series_deconfounder
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [Link]	ICML 2020	alg/discriminative-jackknife
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions [Link]	ICML 2020	alg/rnn-blockwise-jackknife
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [Link]	ICML 2020	alg/transductive_dropout
Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN) [Link]	IEEE	alg/adsgan
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [Link]	NeurIPS 2020	alg/compartmental_gp
Strictly Batch Imitation Learning by Energy-based Distribution Matching [Link]	NeurIPS 2020	alg/edm
Gradient Regularized V-Learning for Dynamic Treatment Regimes [Link]	NeurIPS 2020	alg/grv
CASTLE: Regularization via Auxiliary Causal Graph Discovery [Link]	NeurIPS 2020	alg/castle
OrganITE: Optimal transplant donor organ offering using an individual treatment effect [Link]	NeurIPS 2020	alg/organite
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification [Link]	NeurIPS 2020	alg/r2p-hte
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [Link]	NeurIPS 2020	alg/scigan
Learning outside the Black-Box: The pursuit of interpretable models [Link]	NeurIPS 2020	alg/Symbolic-Pursuit
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain [Link]	NeurIPS 2020	alg/vime
Scalable Bayesian Inverse Reinforcement Learning [Link]	ICLR 2021	alg/scalable-birl
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [Link]	AISTATS 2021	alg/CATENets
Learning Matching Representations for Individualized Organ Transplantation Allocation [Link]	AISTATS 2021	alg/MatchingRep
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [Link]	ICLR 2021	alg/interpole
Inverse Decision Modeling: Learning Interpretable Representations of Behavior [Link]	ICML 2021	alg/ibrc
Policy Analysis using Synthetic Controls in Continuous-Time [Link]	ICML 2021	alg/Synthetic-Controls-in-Continuous-Time
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis [Link]	ICML 2021	alg/organsync
Explaining Time Series Predictions with Dynamic Masks [Link]	ICML 2021	alg/Dynamask
Generative Time-series Modeling with Fourier Flows [Link]	ICLR 2021	alg/Fourier-flows
On Inductive Biases for Heterogeneous Treatment Effect Estimation [Link]	NeurIPS 2021	alg/CATENets
Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation [Link]	NeurIPS 2021	alg/CATENets
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [Link]	NeurIPS 2021	alg/medkit-learn
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [Link]	NeurIPS 2021	alg/MIRACLE
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks [Link]	NeurIPS 2021	alg/DECAF
Explaining Latent Representations with a Corpus of Examples [Link]	NeurIPS 2021	alg/Simplex
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation [Link]	NeurIPS 2021	alg/iTransplant
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [Link]	NeurIPS 2021	alg/Hybrid-ODE-NeurIPS-2021
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes [Link]	NeurIPS 2021	alg/SyncTwin-NeurIPS-2021
Conformal Time-series Forecasting [Link]	NeurIPS 2021	alg/conformal-rnn
Estimating Multi-cause Treatment Effects via Single-cause Perturbation [Link]	NeurIPS 2021	alg/Single-Cause-Perturbation-NeurIPS-2021
Invariant Causal Imitation Learning for Generalizable Policies [Link]	NeurIPS 2021	alg/Invariant-Causal-Imitation-Learning
Inferring Lexicographically-Ordered Rewards from Preferences [Link]	AAAI 2022	alg/lori
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [Link]	ICLR 2022	alg/inverse-online
D-CODE: Discovering Closed-form ODEs from Observed Trajectories [Link]	ICLR 2022	alg/D-CODE-ICLR-2022
Neural graphical modelling in continuous-time: consistency guarantees and algorithms [Link]	ICLR 2022	alg/Graphical-modelling-continuous-time
Label-Free Explainability for Unsupervised Models [Link]	ICML 2022	alg/Label-Free-XAI
Inverse Contextual Bandits: Learning How Behavior Evolves over Time [Link]	ICML 2022	alg/invconban
Data-SUITE: Data-centric identification of in-distribution incongruous examples [Link]	ICML 2022	alg/Data-SUITE
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [Link]	ICML 2022	alg/TE-CDE
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations[Link]	NeurIPS 2022	alg/CARs
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability[Link]	NeurIPS 2022	alg/ITErpretability
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation[Link]	NeurIPS 2022	alg/HTCE-learners
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning[Link]	NeurIPS 2022	alg/HTCE-learners

Details of apps and other software is listed below:

App/Software [Link]	Description	Publication	Code
Adjutorium COVID-19 [Link]	Adjutorium COVID-19: an AI-powered tool that accurately predicts how COVID-19 will impact resource needs (ventilators, ICU beds, etc.) at the individual patient level and the hospital level	-	app/adjutorium-covid19-public
Clairvoyance [Link]	Clairvoyance: A Pipeline Toolkit for Medical Time Series	ICML 2021	clairvoyance repository
Clairvoyance2 [Link]	clairvoyance2: a Unified Toolkit for Medical Time Series	-	clairvoyance2 repository
Hide-and-Seek Privacy Challenge [Link]	Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification with Clinical Time-series Data	NeurIPS 2020 competition track	app/hide-and-seek

Citations

Please cite the the applicable papers and van der Schaar Lab repository if you use the software.

Breakdown by category

See breakdown here.

License

Copyright 2019-2022 van der Schaar Lab.

This software is released under the 3-Clause BSD license unless mentioned otherwise by the respective algorithms and apps.

Installation instructions

See individual algorithm and app directories for installation instructions.

See also doc/install.md for common installation instructions.

Tutorials and or examples

See individual algorithm and app directories for tutorials and examples.

Data

Data files (as well as other large files such as saved models etc.) can be downloaded as per instructions in the DATA-*.md (see e.g. DATA-PUBLIC.md) files found in the corresponding directories.

More info

For more information on the van der Schaar Lab’s work, visit our homepage.

References

See individual algorithm and app directories for references.