Causal Discovery by Interventions via Integer Programming
Authors: Abdelmonem Elrefaey, Rong Pan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate its effectiveness through comparative analysis across different settings to demonstrate its applicability and robustness. ... Table 1 showcases the change in the number of interventions necessary to achieve graph identifiability as a function of N and kmax. |
| Researcher Affiliation | Academia | School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA EMAIL, EMAIL |
| Pseudocode | No | The paper provides mathematical formulations for Integer Programming models (equations 1-11) and describes algorithms like Branch and Bound, Greedy Algorithm, and LP Relaxation and Rounding in paragraph text, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code, nor does it provide links to code repositories or mention code in supplementary materials. |
| Open Datasets | No | The paper does not use or refer to any specific publicly available datasets for its analysis. It uses illustrative examples (e.g., V = {X1, X2, X3, X4}) to demonstrate its methodology but does not conduct experiments on empirical datasets. |
| Dataset Splits | No | The paper does not conduct experiments on any datasets, therefore, there is no discussion of dataset splits. |
| Hardware Specification | No | The paper provides tables with 'solution time in Seconds' but does not specify the hardware (e.g., CPU, GPU models, memory) on which these computations were performed. |
| Software Dependencies | No | The paper mentions Integer Programming (IP) and the Branch and Bound (B&B) algorithm, and generally refers to optimization techniques. It also lists 'CPLEX' as an example for weighted set cover but does not specify any software names with version numbers used for the reported results. |
| Experiment Setup | No | The paper focuses on mathematical formulations and theoretical analysis of intervention sets. It does not describe an experimental setup with hyperparameters, training configurations, or system-level settings, as it does not involve training machine learning models or similar empirical evaluations. |