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..
Sharp Bounds for Generalized Causal Sensitivity Analysis
Authors: Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we propose a scalable algorithm to estimate our sharp bounds from observational data and perform extensive computational experiments to show the validity of our bounds empirically. |
| Researcher Affiliation | Academia | Dennis Frauen, Valentyn Melnychuk & Stefan Feuerriegel LMU Munich Munich Center for Machine Learning EMAIL |
| Pseudocode | Yes | Algorithm 1: Causal sensitivity analysis with mediators |
| Open Source Code | Yes | 1Code is available at https://github.com/DennisFrauen/SharpCausalSensitivity. |
| Open Datasets | Yes | The preprocessed data is publically available at https://github.com/jopersson/covid19-mobility/blob/main/Data. |
| Dataset Splits | No | We perform hyperparameter tuning for our experiments on synthetic data using grid search on a validation set. ... The paper states that a validation set was used for hyperparameter tuning but does not provide specific details on the split percentage or methodology. |
| Hardware Specification | No | The paper does not specify any hardware components (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | We use feed-forward neural networks with softmax activation function... For densities, we use conditional normalizing flows [73] (neural spline flows [21])... We perform training using the Adam optimizer [41]. ... The paper mentions various software components and libraries (e.g., neural networks, normalizing flows, Adam optimizer) but does not provide specific version numbers for them. |
| Experiment Setup | Yes | We perform hyperparameter tuning for our experiments on synthetic data using grid search on a validation set. The tunable parameters and search ranges are shown in Table 2. ... Table 2: MODEL TUNABLE PARAMETERS SEARCH RANGE includes details for Epochs, Batch size, Learning rate, Hidden layer size, Number of spline bins, and Dropout probability. |