PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Authors: Lucile Ter-Minassian, Oscar Clivio, Karla Diazordaz, Robin J. Evans, Christopher C. Holmes
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the resolution, interpretability and true locality of our approach on examples and a real-world experiment. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Oxford, Oxford, UK 2Department of Statistical Science, University College London, London, UK 3The Alan Turing Institute, London, UK. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code used for experiments is available at https://github.com/oscarclivio/pwshap. |
| Open Datasets | Yes | We present a local mediation analysis experiment on the Adult data set from UCI (Asuncion & Newman, 2007)... The URL for this dataset is https://archive.ics.uci.edu/ml/datasets/adult. |
| Dataset Splits | Yes | The 5000 observations in the set were subsampled again 10 times, by taking out 20% of the sample. Each time, we use the subsample as both a training and testing set for our models. |
| Hardware Specification | Yes | Experiments were ran using a 2,6 GHz 6-Core Intel Core i7. |
| Software Dependencies | No | The paper mentions software like “sklearn package” but does not provide specific version numbers for these dependencies. |
| Experiment Setup | No | The paper describes aspects of the experimental setup, such as modeling choices (e.g., “linear and logistic regression”), but does not provide specific hyperparameter values or detailed training configurations in the main text. |