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].
Conditional independences and causal relations implied by sets of equations
Authors: Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We make use of Simon's causal ordering algorithm (Simon, 1953) to construct a causal ordering graph and prove that it expresses the effects of soft and perfect interventions on the equations under certain unique solvability assumptions. We further construct a Markov ordering graph and prove that it encodes conditional independences in the distribution implied by the equations with independent random exogenous variables, under a similar unique solvability assumption. We discuss how this approach reveals and addresses some of the limitations of existing causal modelling frameworks, such as causal Bayesian networks and structural causal models. |
| Researcher Affiliation | Academia | Tineke Blom EMAIL Informatics Institute University of Amsterdam P.O. Box 19268, 1000 GG Amsterdam, The Netherlands Mirthe M. van Diepen EMAIL Institute for Computer and Information Science Radboud University Nijmegen PO Box 9102, 6500 HC Nijmegen, The Netherlands Joris M. Mooij EMAIL Korteweg-De Vries Institute for Mathematics University of Amsterdam P.O. Box 19268, 1000 GG Amsterdam, The Netherlands |
| Pseudocode | Yes | Algorithm 1: Causal ordering using minimal self-contained sets. Algorithm 2: Causal ordering via perfect matching. Algorithm 3: Causal ordering via coarse decomposition. |
| Open Source Code | No | The paper does not provide explicit statements or links to source code for the methodology described. |
| Open Datasets | No | The paper primarily uses a theoretical model of a 'filling bathtub' (Example 1) as an illustrative example, rather than empirical datasets requiring public access information. It discusses using 'observational data' and 'data generated by a simple dynamical model' in a general context but does not analyze or provide specific empirical datasets. |
| Dataset Splits | No | The paper does not conduct experiments on empirical datasets, therefore, no training/test/validation dataset splits are specified. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or hardware specifications used. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithms and proofs; it does not describe an experimental setup, hyperparameters, or training configurations. |