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..
Efficient Causal Decision Making with One-sided Feedback
Authors: Jianing Chu, Shu Yang, Wenbin Lu, PULAK GHOSH
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments and a real-world data application demonstrate the empirical performance of our proposed methods. |
| Researcher Affiliation | Collaboration | Jianing Chu Amazon EMAIL Shu Yang & Wenbin Lu Department of Statistics North Carolina State University EMAIL Pulak Ghosh Indian Institute of Management EMAIL |
| Pseudocode | No | The paper describes methods and theoretical proofs using mathematical notation and text, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing code or a link to a code repository. |
| Open Datasets | No | A simulated dataset based on the real data is available upon request. |
| Dataset Splits | Yes | We consider samples with size n = 1000, 2000. ... We randomly sample the training data with a size 3000 and 5000. The proposed efficient estimator over the entire dataset is used as the testing value. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'random forest (RF) models', 'generalized additive model (GAM)', and a 'tree-based classification algorithm' but does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | We consider a correctly specified logistic regression model for φ(η). We obtain bηnaive using g(x; η) = (1, x1, x2, x3)T . Specifically, in case 1, all the regressions with pseudo-outcomes are using random forest (RF) models. In case 2, we estimate P(Y = 1 | X, A = 1) using a generalized additive model (GAM). For the DR estimator, we estimate w(x) using GAM in both cases. We estimate E(y | x) using RF in case 1 and using GAM in case 2. ... We use a tree-based classification algorithm introduced in Zhou et al. (2023) and focus on depth-2 decision trees for illustration. |