Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Minimum Cost Intervention Design for Causal Effect Identification
Authors: Sina Akbari, Jalal Etesami, Negar Kiyavash
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we generated the causal graphs using the Erdos-Renyi generative model (Erd os & R enyi, 1960) as follows. For a given number of vertices n, we fixed a causal order over the vertices. Then, directed edges were sampled with probability p = 0.35 and bidirected edges were sampled with probability q = 0.25 between the vertices, mutually independently. The set S was selected randomly among the last 5% of the vertices in the causal order such that G[S] is a c-component. Intervention costs of vertices were chosen independently at random from {1, 2, 3, 4}. See Appendix F for further details of the evaluation setup. Our performance measures are runtime and normalized regret. Normalized regret of a given subset A is defined by (C(A) C )/C , where C denotes the optimal min-cost solution. The results are depicted in Figure 3. Each curve and its confidence interval is obtained by averaging over 40 trials. |
| Researcher Affiliation | Academia | Sina Akbari 1 Jalal Etesami 1 Negar Kiyavash 1 1College of Management of Technology, EPFL. Correspondence to: Sina Akbari <sina.akbari@epfl.ch>. |
| Pseudocode | Yes | Algorithm 1 Find Hhull(S, G), G[S] is a c-component. |
| Open Source Code | Yes | The implementations of all the algorithms proposed in this work can be found at https://github.com/Sina Akbarii/min_cost_intervention/tree/main. |
| Open Datasets | Yes | F.1. Benchmark Structures. In this section, we evaluate our algorithms on graphs corresponding to real-world problems, namely the Barley (Kristensen & Rasmussen, 1997), Water (Jensen et al., 1989) and Mehra (Vitolo et al., 2018) structures. |
| Dataset Splits | No | The paper describes the generation of synthetic graphs and the use of benchmark graph structures for evaluation, but it does not specify explicit train/validation/test dataset splits in the conventional sense for machine learning models. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For evaluation, we generated the causal graphs using the Erdos-Renyi generative model (Erd os & R enyi, 1960) as follows. For a given number of vertices n, we fixed a causal order over the vertices. Then, directed edges were sampled with probability p = 0.35 and bidirected edges were sampled with probability q = 0.25 between the vertices, mutually independently. The set S was selected randomly among the last 5% of the vertices in the causal order such that G[S] is a c-component. Intervention costs of vertices were chosen independently at random from {1, 2, 3, 4}. See Appendix F for further details of the evaluation setup. |