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..
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Authors: Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we verify behavior predicted by the theory and compare multiple estimators and score functions to identify the best approaches in practice. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2University of Cambridge, United Kingdom |
| Pseudocode | No | The paper describes the Mechanism Shift Score and its estimand, but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | Yes | All code and experiments are available at https://github.com/rflperry/sparse_shift |
| Open Datasets | No | The paper describes generating synthetic data using an Erd os-Rènyi model and mentions a |
| Dataset Splits | No | The paper mentions using random DAGs over variables with specific edge densities, number of environments, and shift fractions. It also notes using |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions the use of specific software components like "KCI and Fisher-Z are implemented by the causal-learn package [GNU General Public License]" and "causaldag package [3-Clause BSD license]". However, it does not provide specific version numbers for these packages or any other software dependencies, which is necessary for reproducibility. |
| Experiment Setup | Yes | Random DAGs are sampled using an Erd os-Rènyi model [12] in which each edge has some fixed probability of existing (the edge density). In each environment, a random set of variables experience a mechanism change according to a fixed number or fraction of shifts. Each variable j in environment e has a randomly sampled mechanism... three environments are sampled, with 500 samples and two mechanism shifts per environment. |