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..
Deep Counterfactual Estimation with Categorical Background Variables
Authors: Edward De Brouwer
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our construction on three different datasets with different high-dimensional modalities (images and time series) and demonstrate accurate counterfactual estimation. |
| Researcher Affiliation | Academia | Edward De Brouwer ESAT-STADIUS KU Leuven EMAIL |
| Pseudocode | Yes | We also present a pseudo-code description of the procedure in Algorithm 1. |
| Open Source Code | Yes | Our code is available at https://github.com/edebrouwer/cfqp. |
| Open Datasets | No | As counterfactual inference evaluation on real-world data is a complex and ongoing research area, we use synthetic datasets inspired by case studies from the literature. Details about the data generation are given in Appendix A. |
| Dataset Splits | Yes | In Figure 4a, we show the reconstruction error on the validation set (i.e., on the factual data) and the counterfactual reconstruction error of CFQP for different values of K on the image dataset with true number of latent classes K0 = 6. |
| Hardware Specification | No | The paper states that 'The total amount of compute and the type of resources used is available in the Appendix Section B,' but the specific Appendix B content is not provided in the paper excerpt, preventing confirmation of explicit hardware details like GPU/CPU models. |
| Software Dependencies | No | The paper indicates that 'The licenses of all software packages used in our experiments is available at https://anonymous.4open.science/r/cfqp/licenses.txt,' but it does not explicitly list software packages with their specific version numbers within the main text or the provided excerpt. |
| Experiment Setup | No | The paper outlines the training procedure (Initialization, Expectation, Maximization) and mentions 'epochs' in Algorithm 1, and the checklist indicates that 'training details in Section B of the Appendix,' but specific hyperparameter values (e.g., learning rate, batch size) are not explicitly detailed in the provided main text. |