Deep Counterfactual Estimation with Categorical Background Variables
Authors: Edward De Brouwer
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 edward.debrouwer@esat.kuleuven.be |
| 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. |