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..
When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
Authors: Moritz Willig, Tim Woydt, Devendra Singh Dhami, Kristian Kersting
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through expository examples in high-impact domains of medical treatment and judicial decision-making, we highlight the severe consequences that arise when system dynamics are neglected and demonstrate the successful application of meta-causal strategies to navigate these challenges. |
| Researcher Affiliation | Academia | Moritz Willig Department of Computer Science Technical University of Darmstadt Tim Woydt Department of Computer Science Technical University of Darmstadt Devendra Singh Dhami Department of Mathematics and Computer Science Eindhoven University of Technology Kristian Kersting Department of Computer Science Technical University of Darmstadt Hessian Center for AI (hessian.AI) German Research Center for AI (DFKI) |
| Pseudocode | Yes | Algorithm 1 Linearized Meta-Causal Dynamics (LMCD) Algorithm |
| Open Source Code | Yes | Code for reproducing examples is provided at: https://github.com/Moritz Willig/meta Causal Dynamics. |
| Open Datasets | No | We performed simulations over 1,000 repetitions (details given in App. C). All discussed results are averaged values from 1,000 independent roll-outs of the described system. Noise for every roll-out is individually seeded and sampled independently. |
| Dataset Splits | No | We performed simulations over 1,000 repetitions (details given in App. C). The paper uses simulated data and does not mention explicit train/test/validation splits for a dataset. |
| Hardware Specification | Yes | Simulations and visualizations run in under 5 seconds on PC with a AMD Ryzen Threadripper 1900X 8-Core Processor and 32GB of RAM. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | Yes | All structural equations used for the examples in sections 6.1 and 6.2 are provided in appendices C and D. Fully worked examples of a step by step meta-causal analysis on scenarios are given in appendices E and F. Information about seeding an the number of repetitions is stated. For simulations the following exact structural equations where evaluated for 10 discrete time series: [...] The starting values for all variables, except Immune Strength, before advancing to the first timestep are set to 0.0 (and 2.0 for Immune Strength). Timesteps start a zero and are evaluated over 10 time steps, with each timestep representing a two year span and the final step t = 9 ending at age 18. All other values follow from these initial values with conjunction with randomly sampled exposure levels at every timesteps. |