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..
Entropic Causal Inference: Graph Identifiability
Authors: Spencer Compton, Kristjan Greenewald, Dmitriy A Katz, Murat Kocaoglu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rigorously evaluate the performance of our algorithms on synthetic data generated from a variety of models, observing improvement over prior work. Finally we test our algorithms on real-world datasets. |
| Researcher Affiliation | Collaboration | 1Massachusetts Institute of Technology, Cambridge, USA 2MIT-IBM Watson AI Lab, Cambridge, USA 3IBM Research, Cambridge, USA 4Purdue University, West Lafayette, USA. |
| Pseudocode | Yes | Algorithm 1 Learning general graphs with oracle |
| Open Source Code | No | The paper refers to an external repository (bnlearn) which contains data, but does not provide a link or statement about open-sourcing the code for the methodology described in this paper. |
| Open Datasets | Yes | We also apply our algorithms on semi-synthetic data using the bnlearn1 repository and demonstrate the applicability of low-entropy assumptions and the proposed method. 1https://www.bnlearn.com/bnrepository/ |
| Dataset Splits | No | The paper does not provide explicit training/test/validation dataset splits (e.g., percentages or sample counts). It mentions using synthetic and real-world data but not how they are partitioned for different stages. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments (e.g., specific GPU/CPU models, memory details). |
| Software Dependencies | No | The paper mentions 'bnlearn' and 'R' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We evaluate performance via the structural Hamming distance (SHD) from the estimated graph to the true causal graph. See the Appendix for implementation details. The x axis shows entropy of the exogenous noise. The exogenous noise of the first variable is fixed to be large ( 3.3 bits), hence it is a high entropy source (HES). |