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 [1].
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. |