University of Pittsburgh researchers have developed two novel conditional independence tests, the Randomized Conditional Independence Test (RCIT) and the Randomized Conditional Correlation Test (RCoT), to facilitate fast and accurate causal discovery from big data. These tests approximate the Kernel Conditional Independence Test (KCIT) using random Fourier features, significantly improving scalability and performance. RCIT and RCoT enable accurate causal discovery in large datasets, making them practical for various applications in biotechnology, online merchandising, and more.
Description
RCIT and RCoT are designed to address the limitations of existing non-parametric conditional independence tests, which scale poorly with sample size. By utilizing random Fourier features, these tests approximate KCIT while scaling linearly with sample size. This innovation allows for the efficient and accurate computation of p-values, enabling causal discovery algorithms to return accurate graphs with reduced run times. The tests have been shown to perform as well as KCIT in terms of accuracy but with significantly faster processing times, making them suitable for big data applications.
Applications
• Causal discovery in large datasets
• Identifying drug targets in biotechnology
• Optimizing ad placement in online merchandising
• Any field requiring fast and accurate causal inference from big data
Advantages
RCIT and RCoT offer several key advantages, including faster processing times and improved scalability compared to traditional non-parametric conditional independence tests. These tests make non-parametric causal discovery feasible for big data, providing accurate results in a fraction of the time required by existing methods. Their efficiency and accuracy make them valuable tools for researchers and companies working with large datasets.
Invention Readiness
The technology is currently at the software development stage. Initial testing has demonstrated the effectiveness of RCIT and RCoT in providing accurate and fast conditional independence testing for large datasets. Further validation and optimization are ongoing to ensure robustness and applicability across various domains.
