Deep Generalized Method of Moments for Instrumental Variable Analysis
Authors: Andrew Bennett, Nathan Kallus, Tobias Schnabel
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 awb222@cornell.edu Nathan Kallus Cornell University kallus@cornell.edu Tobias Schnabel Microsoft Research tbs49@cornell.edu |
| 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). |