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..
Separators and Adjustment Sets in Markov Equivalent DAGs
Authors: Benito van der Zander, Maciej Liskiewicz
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we provide a new criterion which leads to an efficient algorithmic framework to find, test and enumerate covariate adjustments for chain graphs mixed graphs representing in a compact way a broad range of Markov equivalence classes of DAGs. Sections 5 to 8 analyze the correctness and complexity of the algorithm. |
| Researcher Affiliation | Academia | Benito van der Zander and Maciej Li skiewicz Institute of Theoretical Computer Science, University of L ubeck Ratzeburger Allee 160, 23538 L ubeck, Germany EMAIL |
| Pseudocode | Yes | Function FINDADJSET(G, X, Y) 1. Close G under the rule A B C A B C. If a new v-structure occurs then return and exit. 2. If some chain component of the resulting graph is not chordal then return and exit. 3. Let R denote the resulting graph. 4. Return a set Z satisfying the constructive back-door criterion for R. |
| Open Source Code | Yes | The algorithms are easily implementable and our software is accessible online at http://dagitty.net. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset, thus no information about public availability or access to a dataset is provided. |
| Dataset Splits | No | The paper does not describe empirical experiments or specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper mentions that software is accessible online but does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |