Foundations of Testing for Finite-Sample Causal Discovery
Authors: Tom Yan, Ziyu Xu, Zachary Chase Lipton
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through empirical simulations, we confirm the usefulness of our framework. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA 2Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA. |
| Pseudocode | Yes | Algorithm 1 Causal Discovery Algorithm Template |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper generates synthetic data for its experiments: 'We consider two classes of graphs. (1) Erdos-Renyi graphs with varying number of nodes and density... (2) tree graphs with n... These are used to generate the graph skeleton.' It does not use or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulation configurations with varying interventional samples and trials, but it does not specify traditional training, validation, or test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware (e.g., GPU, CPU models, memory) used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not list any specific software dependencies or versions (e.g., programming languages, libraries, frameworks) required to reproduce the experiments. |
| Experiment Setup | Yes | In the experiment, we fix b = 0.1, variance 1 and the interventional value ν = 1. We vary the number of interventional samples {100, 500, 1000, 5000, 10000}, tolerated error rate α {0.1, 0.2} and edge strength k {0.1, 0.2, 1, 2, 10}, all of which affect hypotheses testing (i.e. number of orientations). |