Causal structure-based root cause analysis of outliers
Authors: Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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 <kaibud@amazon.com>. |
| 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. |