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..
Causal Action Influence Aware Counterfactual Data Augmentation
Authors: Núria Armengol Urpı́, Marco Bagatella, Marin Vlastelica, Georg Martius
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate CAIAC in two goal-conditioned settings: offline RL and offline self-supervised skill learning. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich, Zurich, Switzerland 2Max Planck Institute for Intelligent Systems, Tübingen, Germany 3Department of Computer Science, University of Tübingen, Tübingen, Germany. |
| Pseudocode | Yes | Algorithm 1: CAIAC |
| Open Source Code | Yes | In order to ensure reproducibility of our results, we make our codebase publicly available at https://sites.google.com/view/caiac |
| Open Datasets | Yes | We make use of the data provided in the D4RL benchmark (Fu et al., 2020) |
| Dataset Splits | Yes | All models were trained for 100k gradient steps, and tested to reach low MSE error for the predictions in the validation set (train-validation split of 0.9-0.1). |
| Hardware Specification | Yes | The algorithms were benchmarked on a 12-core Intel i7 CPU. |
| Software Dependencies | No | The paper mentions software components and frameworks like "LMP", "TD3", "TD3+BC", and "Adam optimizer", but it does not specify version numbers for these or for programming languages/libraries like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | For a fine-grained description of all hyperparameters, we refer to our codebase at https://sites.google.com/view/caiac. Also, specific details like "We train each method for 1.2M gradient steps" (Appendix A.1.2) and "αBC = 2.5" (Appendix A.1.3) are provided. |