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..
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Authors: Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our bounds in a series of experiments. |
| Researcher Affiliation | Academia | Valentyn Melnychuk, Dennis Frauen & Stefan Feuerriegel LMU Munich & Munich Center for Machine Learning Munich, Germany |
| Pseudocode | No | The paper describes its neural refutation framework in three stages (Stage 0, Stage 1, Stage 2) but does not provide pseudocode or a formally labeled algorithm block. |
| Open Source Code | Yes | 1Code is available at https://github.com/Valentyn1997/RICB. |
| Open Datasets | Yes | IHDP100 dataset. The Infant Health and Development Program (IHDP) (Hill, 2011; Shalit et al., 2017) is a classical benchmark for CATE estimation... HC-MNIST dataset. HC-MNIST is a semi-synthetic benchmark on top of the MNIST image dataset (Jesson et al., 2021). The MNIST dataset contains ntrain = 60, 000 train and ntest = 10, 000 test images. (Le Cun, 1998) |
| Dataset Splits | Yes | We performed hyperparameter tuning at all the stages of our refutation framework for all the networks based on five-fold cross-validation using the training subset. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory, or cluster specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Py Torch and Pyro' and optimizers like 'Adam W', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Implementation. ... Each network was trained with niter = 5, 000 train iterations. Hyperparameters. We performed hyperparameter tuning at all the stages of our refutation framework for all the networks based on five-fold cross-validation using the training subset. At each stage, we did a random grid search with respect to different tuning criteria. Table 5 provides all the details on hyperparameters tuning. |