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..
Deep Generalized Method of Moments for Instrumental Variable Analysis
Authors: Andrew Bennett, Nathan Kallus, Tobias Schnabel
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results show our algorithm matches the performance of the best tuned methods in standard settings and continues to work in high-dimensional settings where even recent methods break. and In this section, we compare Deep GMM against a wide set of baselines for IV estimation. |
| Researcher Affiliation | Collaboration | Andrew Bennett Cornell University EMAIL Nathan Kallus Cornell University EMAIL Tobias Schnabel Microsoft Research EMAIL |
| Pseudocode | No | The paper describes the algorithm and optimization process but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Our implementation of Deep GMM is publicly available at https://github.com/Causal ML/Deep GMM. |
| Open Datasets | Yes | We now move on to scenarios based on the MNIST dataset [26] in order to test our method s ability to deal with structured, high-dimensional X and Z variables. |
| Dataset Splits | Yes | We sample n = 2000 points for train, validation, and test sets each. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing Deep GMM using PyTorch but does not provide a specific version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | Hyperparameters used for our method in these scenarios are described in Appendix B.2. and The only parameters of our algorithm are the neural network architectures for F and G and the optimization algorithm parameters (e.g., learning rate). |