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..
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Authors: Ahmed M. Alaa, Mihaela van der Schaar
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on observational datasets for an interventional social program applied to premature infants, and a left ventricular assist device applied to cardiac patients wait-listed for a heart transplant. In both experiments, we show that our method significantly outperforms the state-of-the-art. |
| Researcher Affiliation | Academia | Ahmed M. Alaa Electrical Engineering Department University of California, Los Angeles EMAIL Mihaela van der Schaar Department of Engineering Science University of Oxford EMAIL |
| Pseudocode | Yes | Algorithm 1 Causal Inference via CMGPs |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Experiments are conducted using the IHDP dataset introduced in [5]. The UNOS dataset3 The United Network for Organ Sharing (UNOS) dataset contains information on every heart transplantation event in the U.S. since 1987. ... 3https://www.unos.org/data/ |
| Dataset Splits | Yes | We run all the benchmarks with 60/20/20 train-validation-test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of the ADAM gradient descent algorithm but does not specify its version or any other software dependencies with specific version numbers. |
| Experiment Setup | Yes | We run Algorithm 1 with the a learning rate of 0.01 and with the standard setting prescribed in [21] (i.e. β1 = 0.9, β2 = 0.999, ϵ = 10 8). |