Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Resolution of Simpson's paradox via the common cause principle
Authors: Arshak Hovhannisyan, Armen Allahverdyan
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 29,177 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,362 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our study revisits the paradox by re-estimating its frequency with an unbiased data generation process and reaffirms that it is not an artifact of deficient data collection. ... Our numerical result is that the frequency of two inequalities in (52) is 4.29% 0.001%. For this precision it was sufficient to generate N = 107 samples from (48, 50) with n = 8. |
| Researcher Affiliation | Academia | Arshak Hovhannisyan Alikhanyan National Laboratory, Yerevan, Armenia EMAIL Armen Allahverdyan Alikhanyan National Laboratory, Yerevan, Armenia EMAIL |
| Pseudocode | No | The paper describes theoretical models and mathematical derivations, but does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states in its NeurIPS checklist: "Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: As we explained above, the manuscript does not present new experimental results." |
| Open Datasets | Yes | Section 4 considers published data from Ref. [16] on a case of smoking and surviving. ... Section 5 treats data on COVID-19, which was suggested in Ref. [31]. |
| Dataset Splits | No | The paper re-analyzes existing datasets and performs numerical simulations, but does not specify training/test/validation splits in the traditional sense. For the smoking data, it performs coarse-graining of existing age groups: "Now B = {b, b}, b = B1 B2 B3 B4 B5, b = B6" but this is not a train/test split. |
| Hardware Specification | No | The paper describes theoretical and numerical analysis without mentioning specific hardware used for computations or simulations. |
| Software Dependencies | No | The paper does not specify any software libraries, tools, or their version numbers used for the numerical results or analysis. |
| Experiment Setup | Yes | For modeling a non-informative Dirichlet density we find it natural to take α1 = ...αn = 1/n. ... Our numerical result is that the frequency of two inequalities in (52) is 4.29% 0.001%. For this precision it was sufficient to generate N = 107 samples from (48, 50) with n = 8. |