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 [1].
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. |