CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
arXiv:2602.20329v1 Announce Type: new Abstract: This work presents Causal Drift Generator (CaDrift), a time-dependent synthetic data generator framework based on Structural Causal Models (SCMs). The framework produces a virtually infinite combination of data streams with controlled shift events and time-dependent data, making it a tool to evaluate methods under evolving data. CaDrift synthesizes various distributional and covariate shifts by drifting mapping functions of the SCM, which change underlying cause-and-effect relationships between features and the target. In addition, CaDrift models occasional perturbations by leveraging interventions in causal modeling. Experimental results show that, after distributional shift events, the accuracy of classifiers tends to drop, followed by a gradual retrieval, confirming the generator's effectiveness in simulating shifts. The framework has been made available on GitHub.
arXiv:2602.20329v1 Announce Type: new Abstract: This work presents Causal Drift Generator (CaDrift), a time-dependent synthetic data generator framework based on Structural Causal Models (SCMs). The framework produces a virtually infinite combination of data streams with controlled shift events and time-dependent data, making it a tool to evaluate methods under evolving data. CaDrift synthesizes various distributional and covariate shifts by drifting mapping functions of the SCM, which change underlying cause-and-effect relationships between features and the target. In addition, CaDrift models occasional perturbations by leveraging interventions in causal modeling. Experimental results show that, after distributional shift events, the accuracy of classifiers tends to drop, followed by a gradual retrieval, confirming the generator's effectiveness in simulating shifts. The framework has been made available on GitHub.
Executive Summary
The article presents CaDrift, a time-dependent causal generator of drifting data streams based on Structural Causal Models (SCMs). This framework produces synthetic data streams with controlled shift events and time-dependent data, allowing for the evaluation of methods under evolving data conditions. Experimental results demonstrate the generator's effectiveness in simulating shifts, with accuracy of classifiers dropping after distributional shift events and gradually retrieving afterwards. CaDrift addresses the limitation of existing data generation techniques by incorporating time-dependent data and perturbations. The framework's availability on GitHub facilitates further research and development. Overall, CaDrift has the potential to significantly impact the field of data science and machine learning.
Key Points
- ▸ CaDrift is a time-dependent causal generator of drifting data streams based on SCMs.
- ▸ The framework produces synthetic data streams with controlled shift events and time-dependent data.
- ▸ Experimental results demonstrate the generator's effectiveness in simulating shifts.
Merits
Strength in Simulating Real-World Data
CaDrift's ability to generate data streams with controlled shift events and time-dependent data makes it an effective tool for simulating real-world data conditions, which are often characterized by evolving relationships between features and the target.
Flexibility and Customizability
The framework's use of SCMs allows for the synthesis of various distributional and covariate shifts by drifting mapping functions, making it a highly flexible and customizable tool for generating synthetic data.
Demerits
Limited Evaluation of Classification Performance
The article primarily focuses on the generator's effectiveness in simulating shifts, with limited evaluation of classification performance under different shift conditions. Further research is needed to fully assess the framework's impact on classification accuracy.
Assumes Prior Knowledge of SCM
CaDrift assumes prior knowledge of the SCM, which may limit its applicability in scenarios where the underlying causal relationships are unknown or difficult to specify.
Expert Commentary
CaDrift is a significant contribution to the field of data science and machine learning, addressing a critical need for synthetic data generation techniques that can simulate real-world data conditions. The framework's use of SCMs and its focus on time-dependent data and perturbations make it a highly flexible and customizable tool. However, further research is needed to fully evaluate the framework's impact on classification accuracy and to assess its applicability in scenarios where the underlying causal relationships are unknown or difficult to specify. Overall, CaDrift has the potential to significantly impact the field of data science and machine learning, and its availability on GitHub will facilitate further research and development.
Recommendations
- ✓ Further research is needed to fully evaluate the framework's impact on classification accuracy and to assess its applicability in scenarios where the underlying causal relationships are unknown or difficult to specify.
- ✓ The development of more advanced intervention mechanisms in CaDrift could enable the simulation of more complex data conditions, such as non-linear relationships and feedback loops.
