Vector Causal Inference between Two Groups of Variables
Authors: Jonas Wahl, Urmi Ninad, Jakob Runge
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methods empirically and compare them to other state-of-the-art techniques.In Section 5, we analyse the empirical performance of these algorithms in experiments with synthetic data and compare it to that of other approaches (Vanilla-PC and the Trace Method (Janzing et al. 2009; Zscheischler, Janzing, and Zhang 2012)). We also consider a real world climate science example of surface temperatures in the El Ni no Southern Oscillation (ENSO 3.4) region in the pacific and in British Columbia to test our algorithms. |
| Researcher Affiliation | Academia | Jonas Wahl*,1,2, Urmi Ninad*,1,2, Jakob Runge1,2 1Technische Universit at Berlin 2 DLR Institut f ur Datenwissenschaften Jena |
| Pseudocode | Yes | Algorithm 1: 2G-Vec CI.PC |
| Open Source Code | Yes | All code is available at https://github.com/Jonas Choice/ 2GVec CI. |
| Open Datasets | Yes | NCEP-NCAR Reanalysis 1 data was provided by NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov, see Kalnay et al. (1996). |
| Dataset Splits | No | The paper describes the generation of simulated data and the use of real-world data with varying sample sizes, but it does not specify explicit train/validation/test dataset splits, nor does it mention cross-validation or other detailed splitting methodologies for reproducibility. |
| Hardware Specification | Yes | Computations were done on Bull Sequana XH2000 with AMD 7763 CPUs. |
| Software Dependencies | No | The paper mentions using specific tests and algorithms (e.g., 'partial correlation test', 'Gaussian Process distance correlation independence test', 'PC-algorithm') but does not specify version numbers for any software libraries, frameworks, or dependencies used. |
| Experiment Setup | Yes | Models vary along the following parameters: sample size (between 50 and 500); group sizes n and m (between 3 and 100); edge densities within X and ηY (between 1% and 90% of all possible edges); density of the interaction matrix A (between 1% and 90% of all possible entries non-zero); effect size, i.e. size of the entries in A (uniformly randomly drawn from different intervals).In both algorithms, we test for conditional independencies using the partial correlation test at significance level α = 0.01. We choose the sensitivity parameter to be α = 0.01 if not specified differently. |