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].
Treatment Effects Estimation By Uniform Transformer
Authors: Ruoqi Yu, Shulei Wang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The new framework is also applied to several numerical examples to demonstrate its practical merits. ... Practical merits of WUNT are further demonstrated through simulation experiments in Appendix B. |
| Researcher Affiliation | Academia | Ruoqi Yu University of Illinois Urbana-Champaign Champaign, IL 61820, USA EMAIL Shulei Wang University of Illinois Urbana-Champaign Champaign, IL 61820, USA EMAIL |
| Pseudocode | Yes | Algorithm 1. Weighting by Uniform Transformer (WUNT) ... Algorithm 2. Weighting by Uniform Transformer for Average Treatment Effect |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its own source code or a link to a repository for the described methodology. |
| Open Datasets | No | The paper uses simulated data for its experiments, as described in Appendix B, rather than a publicly available dataset. |
| Dataset Splits | No | The paper uses simulated data and multiple replications of simulation experiments, but it does not specify train/validation/test splits for an external dataset. |
| Hardware Specification | Yes | All these algorithms are evaluated with the same laptop (Intel Core i5 @2.3 GHz/8GB). |
| Software Dependencies | No | The paper mentions 'R package random Forest', 'R package CBPS', 'R package ATE', 'R package sbw', and 'mosek' as tools used for comparison, but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In the ο¬rst set of simulation studies, we compare four ways to construct the uniform transformer Ξ¦ from the control samples. ... We vary Ο from 0, 0.1, 0.2 and 0.3. ... The sample size is 500 for the treated group and 1000 for the control group. ... In order to assess the effect of sample size, we vary it from 1000, 2000, and 5000. |