MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Authors: Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation over a variety of benchmark methods across all three missingness scenarios: at random, completely at random, and not at random. |
| Researcher Affiliation | Academia | Trent Kyono University of California, Los Angeles tmkyono@ucla.edu Yao Zhang University of Cambridge yz555@cam.ac.uk Alexis Bellot University of Oxford Oxford, United Kingdom alexis.bellot@eng.ox.ac.uk Mihaela van der Schaar University of Cambridge University of California, Los Angeles The Alan Turing Institute mv472@cam.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code at https://github.com/vanderschaarlab/MIRACLE. |
| Open Datasets | Yes | variety of publicly available UCI datasets [8] |
| Dataset Splits | No | The paper states "We use an 80-20 train-test split" but does not specify a validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, memory, or specific computing environments) used for running its experiments. |
| Software Dependencies | No | The paper mentions "tensorflow" and other libraries like "scikit-learn" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We performed a hyperparameter sweep (log-based) for β1 and β2 with ranges between 1e-3 and 100. By default we have β1 and β2 set to 0.1 and 1, respectively. |