Efficient Causal Decision Making with One-sided Feedback
Authors: Jianing Chu, Shu Yang, Wenbin Lu, PULAK GHOSH
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |