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].
Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
Authors: Lai Wei, Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement the theoretical results by evaluating the proposed approach in a variety of experimental settings featuring different causal structures. We show that our algorithm outperforms the existing baselines in terms of empirical regret. We compare the empirical performance of SCM-AAM with existing algorithms. |
| Researcher Affiliation | Academia | Lai Wei Life Sciences Institute University of Michigan EMAIL Muhammad Qasim Elahi Electrical and Computer Engineering Purdue University EMAIL Mahsa Ghasemi Electrical and Computer Engineering Purdue University EMAIL Murat Kocaoglu Electrical and Computer Engineering Purdue University EMAIL |
| Pseudocode | Yes | Algorithm 1: SCM-based Approximate Allocation Matching (SCM-AAM) |
| Open Source Code | Yes | The algorithm code is provided at https://github.com/Causal ML-Lab/SCM-AAM. |
| Open Datasets | No | The paper describes simulation setups (Task1, Task2, Task3) with structural equations for data generation, but does not use or provide access to a pre-existing public or open dataset. |
| Dataset Splits | No | The paper describes simulation parameters like |
| Hardware Specification | Yes | The simulations were conducted on a Windows desktop computer featuring a 12th generation Intel Core i7 processor operating at 3.1 GHz and 32 GB of RAM. No GPUs were utilized in the simulations. |
| Software Dependencies | No | The paper mentions providing algorithm code but does not list any specific software dependencies or their version numbers within the text. |
| Experiment Setup | Yes | We choose the input parameters of the SCM-AAM algorithm to be = 0.1 and = 1/|5I| in the simulations. We set the horizon to 800 for all three tasks and repeat every simulation 100 times. |