Reproducibility Report for ACM SIGMOD 2024 Paper: 'A Unified Approach for Resilience and Causal Responsibility with Integer Linear Programming (ILP) and LP Relaxations'
We successfully reproduced the experimental results presented in the original paper. The authors provided clear documentation of the software and hardware requirements, along with well-structured scripts and datasets hosted in an organized GitHub repository. In this reproducibility report, we compare the original results with our reproduced findings, both of which show similar trends, thereby supporting the authors' claims.
Authors
- Neha Makhija (ORCID: https://orcid.org/0000-0003-0221-6836)
- Viktor Sanca (ORCID: https://orcid.org/0000-0002-4799-8467)
- Yesdaulet Izenov (ORCID: https://orcid.org/0000-0002-3484-2535)
- Wolfgang Gatterbauer (ORCID: https://orcid.org/0000-0002-9614-0504)
- Amedeo Pachera
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Northeastern University (US)
- Centre National de la Recherche Scientifique (FR)
- Laboratoire d'Informatique en Images et Systèmes d'Information (FR)
- École Polytechnique Fédérale de Lausanne (CH)
- Nazarbayev University (KZ)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1145/3687998.3717049
- Primary Topic
- Bayesian Modeling and Causal Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00