Identification of Time-Dependent Causal Model: A Gaussian Process Treatment
Authors: Biwei Huang, Kun Zhang, Bernhard Schölkopf
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both artificial and real data demonstrate the practical usefulness of time-dependent causal modeling and the effectiveness of the proposed approach for estimation. |
| Researcher Affiliation | Academia | 1 Max Planck Institute for Intelligent Systems, T ubingen, Germany 2 Information Sciences Institute, University of Southern California |
| Pseudocode | No | The paper describes the estimation procedure in prose and mathematical equations but does not present a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or providing links to a code repository for the methodology described. |
| Open Datasets | Yes | This hourly temperature data set was recorded in six places (1 Shed, 2 Outside, 3 Kitchen Boiler, 4 Living Room, 5 WC, 6 Bathroom) of a house in the black forest in Germany. ... [Peters et al., 2013]. |
| Dataset Splits | No | The paper mentions 'cross-validated prediction error' but does not provide specific percentages or counts for training/validation/test dataset splits, nor does it reference predefined splits with explicit details. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions methods like 'Gaussian Process regression' and 'Hilbert-Schmidt Independence Criterion (HSIC)' but does not list specific software dependencies or libraries with version numbers. |
| Experiment Setup | Yes | To use the GP prior, we collect all data points and represent equation (5) in matrix notation: ... We put the GP prior on each time-varying coefficient and confounder term to describe their uncertainty: ... where µ and K (with appropriate subscripts) denote the corresponding mean and covariance in GP (we use a zero mean and squared exponential covariance function), and t is the vector of collected time points. ... We maximize the marginal likelihood to learn the hyperparameters in the mean functions, covariance functions of GP, and the variance σ2 of the noise. |