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..
Causal Identification under Markov Equivalence: Completeness Results
Authors: Amin Jaber, Jiji Zhang, Elias Bareinboim
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive a complete algorithm for identification given a PAG. This implies that whenever the causal effect is identifiable, the algorithm returns a valid identification expression; alternatively, it will throw a failure condition, which means that the effect is provably not identifiable. We further provide a graphical characterization of nonidentifiability of causal effects in PAGs. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, West Lafayette, USA 2Department of Philosophy, Lingnan University, NT, HK. |
| Pseudocode | Yes | Algorithm 1 IDP(x, y) given PAG P |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical contributions (algorithm design, proofs) and does not describe experimental training on datasets. Therefore, it does not mention public datasets or provide access information. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or data splitting. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |