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..
Identifying Macro Causal Effects in a C-DMG over ADMGs
Authors: Simon Ferreira, Charles K. Assaad
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper focuses on causal effect identification within partially specified causal graphs, with particular emphasis on cluster-directed mixed graphs (C-DMGs) which can represent many different acyclic directed mixed graphs (ADMGs)... We establish that the do-calculus is both sound and complete for identifying these effects in C-DMGs over ADMGs... Additionally, we provide a graphical characterization of non-identifiability for macro causal effects in these graphs. All proofs are deferred to the appendix. |
| Researcher Affiliation | Academia | Simon Ferreira EMAIL Sorbonne Université, INSERM, Institut Pierre Louis d Epidémiologie et de Santé Publique, F75012, Paris, France Charles K. Assaad EMAIL Sorbonne Université, INSERM, Institut Pierre Louis d Epidémiologie et de Santé Publique, F75012, Paris, France |
| Pseudocode | No | The paper describes theoretical concepts, definitions, theorems, and proofs related to causal inference in C-DMGs. It does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information, such as a repository link or an explicit statement, for the source code of the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not conduct experiments using specific datasets. Therefore, no information about open or publicly available datasets is provided. |
| Dataset Splits | No | This is a theoretical paper that does not conduct experiments requiring dataset splits. Therefore, no information regarding training, test, or validation splits is provided. |
| Hardware Specification | No | This is a theoretical paper focused on causal inference methodology. It does not describe any experiments or specific hardware used for computations. |
| Software Dependencies | No | This is a theoretical paper focused on causal inference methodology. It does not describe any specific software or libraries with version numbers used for implementation or experiments. |
| Experiment Setup | No | This is a theoretical paper that does not describe an experimental setup, hyperparameters, or system-level training settings, as it does not involve empirical evaluations. |