Separators and Adjustment Sets in Markov Equivalent DAGs
Authors: Benito van der Zander, Maciej Liskiewicz
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 {benito,liskiewi}@tcs.uni-luebeck.de |
| 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. |