Sequential Causal Imitation Learning with Unobserved Confounders
Authors: Daniel Kumor, Junzhe Zhang, Elias Bareinboim
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an efficient 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 dkumor@purdue.edu Junzhe Zhang Columbia University junzhez@cs.columbia.edu Elias Bareinboim Columbia University eb@cs.columbia.edu |
| 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. |