Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Linear Causal Bandits: Unknown Graph and Soft Interventions
Authors: Zirui Yan, Ali Tajer
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 41,692 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,791 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |