Unit Selection with Causal Diagram
Authors: Ang Li, Judea Pearl5765-5772
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we will show how much in general the bounds of the beneļ¬t function are improved by Theorems 1, 2, and 3 in three simple causal diagrams. For each theorem, we randomly generated 100000 sample distributions (observational data and experimental data) compatible with the causal diagram (see the appendix2 for the generating algorithm). |
| Researcher Affiliation | Academia | Ang Li,1 Judea Pearl1 1 Cognitive Systems Laboratory, Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA. |
| Pseudocode | No | The paper contains mathematical formulations and theorems but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link for the appendix (http://ftp.cs.ucla.edu/pub/stat ser/r510.pdf) which contains proofs, but does not provide a link to any open-source code for the described methodology. |
| Open Datasets | No | The paper presents data in Tables 1, 2, and 3 as 'Experimental data collected by the carwash company' and 'Observational data collected by the carwash company' and 'Results of an observational study'. There are no links, DOIs, repository names, or formal citations provided for these datasets to indicate public availability. |
| Dataset Splits | No | The paper does not provide specific training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit mention of cross-validation setup). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud resources used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') that would be needed to reproduce the experiments. |
| Experiment Setup | No | The paper describes how sample distributions were generated for simulations, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or model training configurations. |