CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series (2410.02844v3)

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 paper proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time-series data. The use of interventional data in the causal analysis is crucial for real-world 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 has also been developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.

• The paper introduces CAnDOIT, which integrates interventional data with observational time-series to advance causal discovery methodologies.
• The paper enhances the LPCMCI algorithm to handle latent confounders and time-lagged dependencies, significantly improving metrics like FPR, SHD, and F1 score.
• The paper validates its approach with synthetic data and robotic simulations, demonstrating superior performance in identifying hidden causal relationships.

CAnDOIT: CAusal Discovery with Observational and Interventional Data from Time-Series

The paper "CAnDOIT: CAusal Discovery with Observational and Interventional Data from Time-Series" presents a novel approach for causal discovery that integrates both observational and interventional data within time-series contexts. Authored by Luca Castri, Sariah Mghames, Marc Hanheide, and Nicola Bellotto, the paper addresses shortcomings in existing causal discovery methodologies that primarily rely on purely observational data, thereby limiting causal inference capabilities in dynamic environments.

Overview of the Approach

CAnDOIT enhances the LPCMCI algorithm, which is known for handling time-series data but lacks support for interventional data. The introduction of interventional data is vital, especially in complex real-world applications such as robotics, where reliance solely on observational data often results in incomplete causal models.

Key innovations in CAnDOIT include:

• Integration of Interventional Data: By using context variables, CAnDOIT models interventions without altering the underlying causal structure between observational and interventional states. This design leverages the JCI framework, allowing context variables to act as meta-parameters. These variables enable the modeling of interventions by creating a dummy exogenous variable that injects the interventional data while maintaining dependencies in the causal graph.
• Algorithmic Enhancements: The paper makes substantial modifications to LPCMCI, empowering it to better handle latent confounders and time-lagged dependencies. The algorithm starts with a fully connected graph and iteratively constrains it through orientation rules, enhancing the accuracy and efficiency of causal structure estimation.

Evaluation and Results

The robustness of CAnDOIT is empirically validated using both synthetic and real-world data:

1. Synthetic Data Evaluation: Utilizing a custom random-model generator, the evaluation showcases the algorithm's effectiveness across various scenarios, including linear and nonlinear systems with different levels of complexity. The results indicate significant improvements in False Positive Rate (FPR), Uncertainty, and PAG Size metrics compared to LPCMCI. The structural accuracy, captured by SHD and F1 Score, also demonstrates CAnDOIT’s superior performance.
2. Robotic Scenario: Demonstrating practical applicability, the algorithm is tested in a simulated robotic environment using Causal World. Here, CAnDOIT effectively identifies causal structures previously obscured by latent variables when interventional data is included, further establishing its utility in dynamic real-world applications.

Implications and Future Directions

CAnDOIT sets a new standard for causal discovery in time-series data by incorporating interventional information, which has been largely unexplored in this context. The practical implications are broad, potentially revolutionizing fields where understanding causal mechanisms is crucial, such as robotics and healthcare.

Theoretically, CAnDOIT provides a novel methodological framework that can be expanded to include diverse types of interventions, known or unknown targets, and varying computational models. As the demand for more nuanced causal inference grows, especially in intelligent systems, CAnDOIT presents a promising direction for future research.

Continued advancements could see the algorithm extended to accommodate soft interventions and explore its adaptability to larger-scale problems. Additionally, optimizing data ratios for observational and interventional inputs may further enhance its application scope.

Conclusion

CAnDOIT represents a significant methodological advancement in causal discovery for time-series data, offering enhanced accuracy by integrating interventional insights. The research underscores the potential of combining observational and interventional data, paving the way for transformative developments in intelligent systems and various scientific domains. The tool’s availability on GitHub ensures that it will serve as a resource for further research and application in complex systems analysis.