Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Authors: Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |