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..
Instrumental Variable Estimation of Average Partial Causal Effects
Authors: Yuta Kawakami, Manabu Kuroki, Jin Tian
ICML 2023 | Venue PDF | 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. |