Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Authors: Ioana Bica, James Jordon, Mihaela van der Schaar
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,800 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,844 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments section, we introduce a new semi-synthetic data simulation for use in the continuous intervention setting and demonstrate improvements over the existing benchmark models. We show, using semi-synthetic experiments, that our model outperforms existing benchmarks. The addition of each component results in improved performance, with the ο¬nal row (our full model) demonstrating the best performance across both datasets and for all metrics. |
| Researcher Affiliation | Academia | Ioana Bica Department of Engineering Science University of Oxford, Oxford, UK The Alan Turing Institute, London, UK EMAIL James Jordon Department of Engineering Science University of Oxford, Oxford, UK EMAIL Mihaela van der Schaar University of Cambridge, Cambridge, UK University of California, Los Angeles, USA The Alan Turing Institute, London, UK EMAIL |
| Pseudocode | Yes | Pseudo-code for our algorithm can be found in Appendix D. |
| Open Source Code | Yes | The implementation of SCIGAN can be found at https://bitbucket.org/mvdschaar/ mlforhealthlabpub/ and at https://github.com/ioanabica/SCIGAN. |
| Open Datasets | Yes | We obtain features, x, from a real dataset (in this paper we use TCGA [22], News [19, 23]) and MIMIC III [24]) |
| Dataset Splits | No | The paper mentions training datasets and evaluates performance, but it does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes some aspects of the experimental setup, such as parameters for data generation and the default value of 'nw', and mentions hyperparameter optimization for benchmarks. However, it does not provide specific experimental setup details like concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) for its own model. |