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..
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Authors: Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic, semi-synthetic and real datasets demonstrate the merits of our approach. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, T ubingen, Germany 2Technion, Israel Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Kernel conditional discrepancy (KCD) test of conditional distributional treatment effect |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We demonstrate the use of our methods on the Infant Health and Development Program (IHDP) dataset (Hill, 2011, Section 4). |
| Dataset Splits | No | The paper describes the IHDP dataset and how outcomes are simulated, but it does not specify explicit train/validation/test split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Python' and the 'Falkon library' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |