Luca Castri, Sariah Mghames, Marc Hanheide, and Nicola Bellotto
Advanced Intelligent Systems
Abstract
The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional timeseries data. The use of interventional data in the causal analysis is crucial for realworld applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well-known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.
@article{https://doi.org/10.1002/aisy.202400181,
author = {Castri, Luca and Mghames, Sariah and Hanheide, Marc and Bellotto, Nicola},
title = {CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series},
journal = {Advanced Intelligent Systems},
volume = {n/a},
number = {n/a},
pages = {2400181},
keywords = {causal robotics, observations and interventions-based causal discoveries, time series},
doi = {https://doi.org/10.1002/aisy.202400181},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202400181},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202400181},
}