Instrumental Variable Estimation of Average Partial Causal Effects
Authors: Yuta Kawakami, Manabu Kuroki, Jin Tian
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate them on synthetic and real-world data. |
| Researcher Affiliation | Academia | 1Department of Mathematics, Physics, Electrical Engineering and Computer Science, Yokohama National University, Yokohama, Kanagawa, JAPAN 2Department of Computer Science, Iowa State University, Ames, Iowa, USA. |
| Pseudocode | Yes | Algorithm 1 Nonparametric APCE (N-APCE) estimator. Algorithm 2 Parametric APCE (P-APCE) estimator. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is open-sourced or publicly available. |
| Open Datasets | Yes | We take up an open dataset in the R package wooldridge (https://cran.r-project. org/package=wooldridge), which was analyzed by Griliches (1977) and Blackburn & Neumark (1992). |
| Dataset Splits | Yes | To determine the best degree of the model, we separate the data set into training set D and validation set D , estimate ˆθ by the training set, and evaluate the trained model using the performance measure (19). |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or cloud computing instances with detailed specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'R package wooldridge' but does not provide a specific version number. No other software components are mentioned with version details. |
| Experiment Setup | Yes | Settings of N-APCE (Algorithm 1) We let X = {0, 0.3, . . . , 2.7, 3}; and the N-APCE estimator at X = 0 is not defined since x0 is 0. We calculate the numerical integration using the left-hand rule. We let the initial function ˆθ1 be a zero function, and the stop threshold ϵ be 10. We choose the step size as the smallest one from (1, 0.5, 0.1, . . .) when Algorithm 1 stops before 100 iterations, and the chosen step size α is 0.5. Settings of P-APCE (Algorithm 2) We use the polynomial basis functions ϕp(x) = xp 1 for p = 1, 2, . . ., and calculate the solution of the equation (18) by ( ˆDT ˆD) 1 ˆDT ˆc. To determine the best degree of the model, we separate the data set into training set D and validation set D , estimate ˆθ by the training set, and evaluate the trained model using the performance measure (19). From the results (shown in Table 5 in the appendix), we decide that the highest degree of the polynomial functions in the P-APCE estimator will be 3, when the mean of the performance measure is the smallest. |