Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sequential Causal Imitation Learning with Unobserved Confounders
Authors: Daniel Kumor, Junzhe Zhang, Elias Bareinboim
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an ef๏ฌcient algorithm for determining imitability and corroborate our theory with simulations. We performed 2 experiments (for full details, refer to Kumor et al. (2021, Appendix B)), comparing the performance of 4 separate approaches to determining which variables to include in an imitating policy: |
| Researcher Affiliation | Academia | Daniel Kumor Purdue University EMAIL Junzhe Zhang Columbia University EMAIL Elias Bareinboim Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 Find largest valid OX in ancestral graph of Y given G, X and target Y |
| Open Source Code | No | The paper references a technical report for full details but does not provide a direct link to open-source code for the methodology described. |
| Open Datasets | Yes | The second simulation used a synthetic, adversarial causal model, enriched with continuous data from the High D dataset (Krajewski et al., 2018) altered to conform to the causal model |
| Dataset Splits | No | The paper does not specify exact training, validation, and test dataset splits (percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'A neural network was trained for each action-policy pair using standard supervised learning approaches' but does not specify any software or library names with version numbers. |
| Experiment Setup | No | The paper mentions training a neural network using 'standard supervised learning approaches' but does not provide concrete hyperparameters (e.g., learning rate, batch size, number of epochs) or other detailed system-level training settings. |