SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes
Authors: Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to extensive simulations, we conduct an observational study using real EHR data and successfully reproduced the findings of a randomized controlled clinical trial with Sync Twin. |
| Researcher Affiliation | Collaboration | Zhaozhi Qian University of Cambridge zq224@cam.ac.uk Yao Zhang University of Cambridge yz555@cam.ac.uk Ioana Bica University of Oxford The Alan Turing Institute ioana.bica@eng.ox.ac.uk Angela Mary Wood University of Cambridge amw79@medschl.cam.ac.uk Mihaela van der Schaar University of Cambridge UCLA The Alan Turing Institute mv472@cam.ac.uk |
| Pseudocode | Yes | The pseudocode is described in A.7. |
| Open Source Code | Yes | The implementation of Sync Twin and the experiment code are available at https:// github.com/Zhaozhi QIAN/Sync Twin-Neur IPS-2021 or https://github.com/orgs/ vanderschaarlab/repositories |
| Open Datasets | Yes | We used medical records from English National Health Service general practices that contributed anonymised primary care electronic health records to the Clinical Practice Research Datalink (CPRD), covering approximately 6.9 percent of the UK population [25]. |
| Dataset Splits | Yes | They were split into three equally-sized subsets for training, validation and testing, each with 17,371 treated and 24,557 controls. |
| Hardware Specification | No | Our text does not contain specific details about the hardware used to run the experiments, such as exact GPU or CPU models, or memory specifications. |
| Software Dependencies | No | Our text does not contain specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | No | Our text does not contain specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs) or detailed training configurations. |