Fast Proxy Experiment Design for Causal Effect Identification
Authors: Sepehr Elahi, Sina Akbari, Jalal Etesami, Negar Kiyavash, Patrick Thiran
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present numerical experiments that showcase the empirical performance and time efficiency of our proposed exact and heuristic algorithms. |
| Researcher Affiliation | Academia | Sepehr Elahi EPFL, Switzerland sepehr.elahi@epfl.ch Sina Akbari EPFL, Switzerland sina.akbari@epfl.ch Jalal Etesami TUM, Germany j.etesami@tum.de Negar Kiyavash EPFL, Switzerland negar.kiyavash@epfl.ch Patrick Thiran EPFL, Switzerland patrick.thiran@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Intervention design for generalized adjustment |
| Open Source Code | No | We will release our code as a toolbox on Git Hub, providing access for researchers and practitioners. The link will be available upon publication of the paper. |
| Open Datasets | Yes | We ran each algorithm for solving the MCID problem on 100 randomly generated Erdos-Renyi [Erdos and Renyi, 1960] ADMG graphs with directed and bidirected edge probabilities ranging from 0.01 to 1.00, in increments of 0.01. We conduct experiments using 17 real-world networks from the Bayesian Network Repository8. This repository encompasses networks from diverse domains such as biology, engineering, medicine, and social science. Footnote 8: bnlearn.com/bnrepository |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, or test splits. It mentions 'synthetic and real-world experiments' but lacks details on data partitioning. |
| Hardware Specification | Yes | All experiments, coded in Python, were conducted on a machine equipped two Intel Xeon E5-2680 v3 CPUs, 256GB of RAM, and running Ubuntu 20.04.3 LTS. |
| Software Dependencies | No | Our codebase is implemented fully in Python. We use the Py SAT library for formulating and solving the WPMAX-SAT problem, and the Pu LP library for formulating and solving the ILP problem. We used the RC2 algorithm [Ignatiev et al., 2019], and the Gurobi solver [Gurobi Optimization, LLC, 2023], to solve the WPMAX-SAT problem, and the ILP, respectively. |
| Experiment Setup | Yes | We ran each algorithm for solving the MCID problem on 100 randomly generated Erdos-Renyi [Erdos and Renyi, 1960] ADMG graphs with directed and bidirected edge probabilities ranging from 0.01 to 1.00, in increments of 0.01. We performed two sets of simulations: for single-district and multiple-district settings, respectively. In the single-district case, we varied n, the number of vertices, from 20 to 100, while in the multiple-district case, we fixed n = 20 and varied the number of districts from 1 to 9. |