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..
Estimating Identifiable Causal Effects through Double Machine Learning
Authors: Yonghan Jung, Jin Tian, Elias Bareinboim12113-12122
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results corroborate with the theory. ... We evaluate the proposed estimators on the models in Examples 1 and 2. |
| Researcher Affiliation | Academia | Yonghan Jung1, Jin Tian2, Elias Bareinboim 3 1 Department of Computer Science, Purdue University 2 Department of Computer Science, Iowa State University 3 Department of Computer Science, Columbia University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: DML-ID (x, y, G, P) ... Algorithm 2: COMPONENTUIF(Aj, Mj r) |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | The causal graphs are constructed from the classic Alarm network (Beinlich et al. 1989), originally collected from a system used to monitor patients conditions. |
| Dataset Splits | No | The paper mentions 'Split D randomly into two halves: D0 and D1' for cross-fitting, but does not provide specific train/validation/test percentages, sample counts, or explicit cross-validation setups for general reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Nuisance functions are estimated using gradient boosting models called XGBoost (Chen and Guestrin 2016)' but does not provide a specific version number for XGBoost or any other software. |
| Experiment Setup | No | The paper states 'Details of the models and the data-generating process are described in Appendix B.' indicating that specific experimental setup details (like hyperparameters or training schedules) are not provided in the main text. |