Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Authors: Ahmed M. Alaa, Mihaela van der Schaar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 ahmedmalaa@ucla.edu Mihaela van der Schaar Department of Engineering Science University of Oxford mihaela.vanderschaar@eng.ox.ac.uk |
| 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). |