Minimum Cost Intervention Design for Causal Effect Identification
Authors: Sina Akbari, Jalal Etesami, Negar Kiyavash
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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. |