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 structure-based root cause analysis of outliers
Authors: Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the empirical performance of the method through simulations and present a real-world case study identifying root causes of extreme river flows. |
| Researcher Affiliation | Industry | 1Amazon Research Tübingen. Correspondence to: Kailash Budhathoki <EMAIL>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation is available from the gcm module (Bl obaum et al., 2022) in Do Why. |
| Open Datasets | Yes | Data Source: https://tinyurl.com/ukriverdata |
| Dataset Splits | No | The paper only mentions 'training samples' and 'test samples' but does not specify a separate validation split or explicit validation methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions 'gcm module... in Do Why' but does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, or the mentioned modules). |
| Experiment Setup | Yes | To each node Xj, we assign a random linear structural equation of the form Xj := P i βij PAij + Nj, where PAij is the i-th component of Xj s parents PAj, βij Uniform(0, 5) and Nj Gaussian(0, 1). ... With a threshold of z = 3, we identify four outliers. |