Unit Selection Based on Counterfactual Logic
Authors: Ang Li, Judea Pearl
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, by simulation, we demonstrate that sets of individuals selected by the derived criterion yield greater overall benefit than those selected by standard methods.In this section, we present two simulated examples, one to demonstrate that the midpoints of the bounds of the objective function given by equations (3, 4) are adequate for selecting the desired individuals, and one to demonstrate the case that satisfies gain equality. |
| Researcher Affiliation | Academia | Ang Li and Judea Pearl Cognitive Systems Laboratory, University of California, Los Angeles {angli, judea}@cs.ucla.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code related to the methodology described. |
| Open Datasets | No | The paper uses simulated data for its examples (e.g., "We randomly select 700 customers from each group and offer the special renewal deal to 350 customers in each group."). It does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes simulated examples but does not provide specific dataset split information (e.g., exact percentages, sample counts for training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical derivations and simulations to demonstrate a concept, but it does not provide specific experimental setup details, such as concrete hyperparameter values or training configurations. |