Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets
Authors: Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour10153-10161
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods. 7 Experimental Results To show the efficacy of the proposed approach for causal discovery from non-identical data sets, we apply it to both synthetic and real-world data. |
| Researcher Affiliation | Academia | 1Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA 2School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia |
| Pseudocode | Yes | Algorithm 1 Adversarial Learning-Based Estimation of the Causal Adjacency Matrix |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | We applied CD-Mi Ni to multivariate flow cytometry data, which were measured from 11 phosphorylated proteins and phospholipids (Sachs et al. 2005). |
| Dataset Splits | No | The paper does not explicitly provide details about standard training, validation, or test dataset splits (e.g., percentages or counts for each split). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods but does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | Each noise term ei is modeled with a mixture of two Gaussian components, with mean μi,k U( 0.6, 0.3) U(0.3, 0.6), variance σ2 i,k U(0.1, 0.5), and the mixture proportion πi,k U(0.3, 0.6) with 2 k=1 πi,k = 1. The non-zero entries of the causal adjacency matrix B was generated according to bij U( 0.8, 0.3) U(0.3, 0.8). For the results from the proposed methods, the final graph is determined by setting a threshold on the estimated causal adjacency matrix ˆB; we used 0.1 as the threshold, that is, the estimated graph ˆGij = 1 if |ˆbij| > 0.1, and ˆGij = 0 if otherwise. |