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 on Markov Equivalence Class through Double Machine Learning
Authors: Yonghan Jung, Jin Tian, Elias Bareinboim
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results corroborate with the theory. and Experimental studies corroborate with the theory. and 6. Experiments We evaluate DML-IDP for estimating Px(y) in Fig. (2a,2b,1). We specify an SCM M for each PAG and generate datasets D from M. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, USA 2Department of Computer Science, Iowa State University, USA 3Department of Computer Science, Columbia University, USA. |
| Pseudocode | Yes | Algorithm 1 IFP(x, y, G(V), P) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | No | We specify an SCM M for each PAG and generate datasets D from M. and All values in the following tables denote parameters for beta distributions, and we sample from each variable according to the parameters. (No concrete access information for a publicly available or open dataset is provided.) |
| Dataset Splits | No | We generate 100 datasets for each sample size N. (No specific dataset splits like percentages or absolute counts for training, validation, and test sets are mentioned.) |
| Hardware Specification | No | We use a single CPU core for XGBoost training and prediction. (No specific hardware details like CPU model, GPU model, or memory specifications are provided.) |
| Software Dependencies | No | Nuisance functions are estimated using standard techniques available in the literature (refer to Appendix C for details), e.g., conditional probabilities are estimated using a gradient boosting model XGBoost (Chen & Guestrin, 2016) and The nuisance functions are estimated with XGBoost (Chen & Guestrin, 2016). (XGBoost is mentioned, but no specific version number is provided.) |
| Experiment Setup | Yes | XGBoost is set with default parameters with max_depth=6, subsample=0.8, colsample_bytree=0.8, and n_estimators=100. We use a single CPU core for XGBoost training and prediction. Each experiment is repeated 100 times, and we compute the average AAE. |