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 [1].
Linear Causal Bandits: Unknown Graph and Soft Interventions
Authors: Zirui Yan, Ali Tajer
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we assess the regret performance of GA-LCB. As the most relevant existing approaches, we compare the regret of our algorithm to those of Lin SEM-UCB [15] and GCB-UCB [18], which are designed for causal bandits with soft interventions. ... Figure 2: Cumulative regret with L = 2. |
| Researcher Affiliation | Academia | Zirui Yan Rensselaer Polytechnic Institute EMAIL Ali Tajer Rensselaer Polytechnic Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Graph-Agnostic Linear Causal Bandit: Structure Learning (GA-LCB-SL) ... Algorithm 2 Graph-Agnostic Linear Causal Bandit: Intervention Design (GA-LCB-ID) |
| Open Source Code | Yes | See the details in Appendix A and the codes in https://github.com/ Zirui Yan/Linear-Causal-Bandit-Unknown-Graph. |
| Open Datasets | No | Causal graph. We consider the hierarchical graph illustrated in Figure 1. This graph consists of (L + 1) layers, with the first L layers having d nodes. ... The noise terms {ϵi : i [N]} are set to be drawn from the uniform distribution Unif(0, 1). |
| Dataset Splits | No | The paper describes generating synthetic data based on a hierarchical graph and setting parameters (e.g., noise terms, weight matrices, exploration times T1 and T2), but does not define traditional train/validation/test dataset splits. |
| Hardware Specification | Yes | The experiment was conducted using 2 CPUs from Mac Mini 2023. |
| Software Dependencies | No | The paper does not list specific versions for any software components, libraries, or programming languages (e.g., Python, PyTorch) that would be necessary for reproducible replication. |
| Experiment Setup | Yes | We set the non-zero elements in the observational and interventional weights matrix to 1 and 0.5, respectively. ... We set T1 = T2 = 500 for experiments with L = 2, T1 = T2 = 1000 for experiments with L = 4 and L = 6. ... We observe that setting λ = α = 0.1 yields reasonable performance. |