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..
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
Authors: Patrik Reizinger, Siyuan Guo, Ferenc Huszar, Bernhard Schölkopf, Wieland Brendel
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate that both cause and mechanism variability enable causal structure identification, we ran synthetic experiments based on the publicly available repository of the Causal de Finetti paper5. [...] Results. Fig. 6 shows the proportion of correctly identified causal structures for different numbers of environments. The Causal-de-Finetti algorithm outperforms all the other methods with an accuracy close to 100%. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2University of Cambridge, Cambridge, United Kingdom 3ELLIS Institute Tübingen, Tübingen, Germany 4Tübingen AI Center, Tübingen, Germany |
| Pseudocode | No | The paper describes theoretical concepts and mathematical derivations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https:// github.com/rpatrik96/IEM |
| Open Datasets | Yes | To demonstrate that both cause and mechanism variability enable causal structure identification, we ran synthetic experiments based on the publicly available repository of the Causal de Finetti paper5. [...] The Cd F parameters N = [ψ, θ] were randomly generated with distinct and independent elements in each environment. Samples within each environment have the noise variables S generated via Laplace distributions conditioned on the corresponding Cd F parameters i.e., the Cd F parameter is the location (mean) of the Laplace distribution. |
| Dataset Splits | Yes | We use, as in the original code, two samples per environment and ablate over {100, 200, 300, 400, 500} environments. Each experiment is repeated 100 times. |
| Hardware Specification | No | The paper mentions "compute resources at the Tübingen Machine Learning Cloud" in the acknowledgments but does not provide specific hardware models (e.g., CPU/GPU types, memory). |
| Software Dependencies | No | The paper mentions not using a "Mat Lab license" for a comparison method (CD-NOD) but does not specify any software dependencies with version numbers for their own implementation. |
| Experiment Setup | Yes | We ran synthetic experiments based on the publicly available repository of the Causal de Finetti paper5. [...] The Cd F parameters N = [ψ, θ] were randomly generated with distinct and independent elements in each environment. Samples within each environment have the noise variables S generated via Laplace distributions conditioned on the corresponding Cd F parameters i.e., the Cd F parameter is the location (mean) of the Laplace distribution. [...] We measure causal structure identification by three conditional independence tests with a significance level of α = 0.05. We choose the estimated causal structure to be the one corresponding to the test with the highest p-value. |