Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
Authors: Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we confirm the soundness of our proposed method. We implement the following three methods based on our proposed method: first, we use neural networks to predict f and train the model by penalized least-squares in (4) (IW-LS); second, we use neural networks to predict f and train the model by minimizing the sum of approximated moment restrictions in (2) (IW-MM), which is the same as IW-LS except for the penalized term in the IW-LS; third, we use a linear-in-parameter model with the Gaussian kernel to predict f and train the model by GMM (IW-Krnl). For all cases, we use neural networks for estimating r . We compare our proposed methods with four methods: Deep GMM (Bennett et al. (2019)), DFIV (Xu et al. (2021a)), Deep IV (Hartford et al. (2017)), and KIV (Singh et al. (2019)). We use the datasets proposed in Newey & Powell (2003), Ai & Chen (2003), and Hartford et al. (2017). |
| Researcher Affiliation | Collaboration | Masahiro Kato1,2, Masaaki Imaizumi2, Kenichiro Mc Alinn3, Shota Yasui1, and Haruo Kakehi1 1AI Lab, Cyber Agent, Inc. 2The University of Tokyo 3Temple University |
| Pseudocode | No | The paper describes methods in prose and mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | For Deep GMM, DFIV, Deep IV, and KIV, we use the code and hyperparameters used in Xu et al. (2021a)1. 1https://github.com/liyuan9988/Deep Feature IV. The paper does not provide a link or statement that their own proposed methods' code is open-source. |
| Open Datasets | Yes | We use the datasets proposed in Newey & Powell (2003), Ai & Chen (2003), and Hartford et al. (2017). |
| Dataset Splits | No | The paper mentions using specific sample sizes (e.g., 'n = 1,000', '1,000 samples', '5,000 samples') for experiments and calculating MSE, but does not provide explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not specify any hardware details like GPU models, CPU types, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'neural networks' and methods like 'least-squares', but it does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | A regularization coefficient η is set to 0.001 as a result of cross-validation. Each fully-connected layer (FC) is followed by leaky Re LU activations with leakiness α = 0.2. For Deep GMM, DFIV, Deep IV, and KIV, we use the code and hyperparameters used in Xu et al. (2021a). |