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..
Towards Trustworthy Explanation: On Causal Rationalization
Authors: Wenbo Zhang, Tong Wu, Yunlong Wang, Yong Cai, Hengrui Cai
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets with extensive experiments compared to state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Department of Statistics, University of California Irvine, California, USA 2Advanced Analytics, IQVIA, Pennsylvania, USA. |
| Pseudocode | Yes | Algorithm 1 Causal Rationalization |
| Open Source Code | Yes | Our code is publicly available online.1 |
| Open Datasets | Yes | Beer Review Data. We use the publicly available version of the Beer review dataset also adopted by Bao et al. (2018) and Chen et al. (2022). |
| Dataset Splits | Yes | We follow the same train/validation/test split as Chen et al. (2022) and it is summarized in Table 5. |
| Hardware Specification | Yes | All of our experiments are conducted with PyTorch on 4 V100 GPU. |
| Software Dependencies | No | The paper mentions "PyTorch" and "BERT-base-uncased" but does not specify their version numbers. |
| Experiment Setup | Yes | For all experiments, we utilize a batch size of 256 and choose the learning rate ฮฑ {1e-5, 5e-4, 1e-4}. We train for 10 epochs all the datasets. For training the causal component, we tune the values of the Lagrangian multiplier ยต {0.01, 0.1, 1} and set k = 5. We set the temperature of Gumbel-softmax to be 0.5. |