Kernel Instrumental Variable Regression
Authors: Rahul Singh, Maneesh Sahani, Arthur Gretton
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, KIV outperforms state of the art alternatives for nonparametric IV regression. We compare the empirical performance of KIV (Kernel IV) to four leading competitors: standard kernel ridge regression (Kernel Reg) [50], Nadaraya-Watson IV (Smooth IV) [16, 23], sieve IV (Sieve IV) [48, 17], and deep IV (Deep IV) [36]. |
| Researcher Affiliation | Academia | Rahul Singh MIT Economics rahul.singh@mit.edu Maneesh Sahani Gatsby Unit, UCL maneesh@gatsby.ucl.ac.uk Arthur Gretton Gatsby Unit, UCL arthur.gretton@gmail.com |
| Pseudocode | Yes | Algorithm 1. Let X and Z be matrices of n observations. Let y and Z be a vector and matrix of m observations. W = KXX(KZZ + nλI) 1KZ Z, ˆ = (WW 0 + m KXX) 1W y, ˆhm (x) = (ˆ )0KXx where KXX and KZZ are the empirical kernel matrices. |
| Open Source Code | Yes | Code: https://github.com/r4hu1-5in9h/KIV |
| Open Datasets | No | We implement each estimator on three designs. The linear design [17] involves learning counterfactual function h(x) = 4x 2, given confounded observations of continuous variables (X, Y ) as well as continuous instrument Z. The sigmoid design [17] involves learning counterfactual function h(x) = ln(|16x 8| + 1) sgn(x 0.5) under the same regime. The demand design [36] involves learning demand function h(p, t, s) = 100 + (10 + p) s (t) 2p... The paper references designs from other papers but does not provide direct links or access information for these datasets. |
| Dataset Splits | No | Sample splitting in this context means estimating stage 1 with n randomly chosen observations and estimating stage 2 with the remaining m observations. In Appendix A.5.2, we provide a validation procedure to empirically determine values for (λ, ). While sample splitting is mentioned and a validation procedure is noted, the paper does not specify the explicit training/validation/test dataset percentages, absolute sample counts, or reference predefined splits for the empirical experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as programming language versions or library versions. |
| Experiment Setup | No | For each algorithm, design, and sample size, we implement 40 simulations and calculate MSE with respect to the true structural function h. In Appendix A.11 for representative plots, implementation details, and a robustness study. The main text refers to implementation details being in the appendix, but does not provide specific hyperparameter values, training configurations, or system-level settings within the main body. |