Causal Invariance Feature Selection for Robust Prediction
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Solution Overview
Problem
Existing data processing methods struggle to accurately predict causality between features across different environments due to varying data distributions, leading to poor prediction results in fields like user service, healthcare, and online advertising.
Innovation Solution
The method identifies features with causal invariance across different environments and trains a prediction model using these features, ensuring that the target feature can be accurately predicted in new environments by maintaining consistent impact across varying distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to predict causality between features, then the prediction process can be completed, but the prediction accuracy deteriorates due to varying data distributions across different environments
Solution Approach 1:
The patent segments the feature set into two distinct categories: features with causal invariance and features without causal invariance. This segmentation is achieved through causal discovery algorithms that identify which features maintain consistent causal relationships across different environments. By treating these two types of features differently in the prediction model, the system achieves both high prediction accuracy and environmental adaptability.
Solution Approach 2:
The patent dynamically adjusts the prediction model by selectively using features with causal invariance when deploying to new environments. The system transitions from a static feature set to a dynamic selection process where only invariant features are utilized for predictions in unseen environments, enabling the model to adapt to distribution shifts while maintaining accuracy.
2Reliability
If all available features are used for training the prediction model, then the model can be trained with sufficient data, but the prediction reliability deteriorates when applied to new environments with different data distributions
Solution Approach 1:
The patent extracts and isolates the subset of features that possess causal invariance from the complete feature set. Through causal discovery and invariance detection mechanisms, the system separates the reliable invariant features from the non-invariant ones. This extraction process ensures that only the most reliable features are used for training and prediction, improving generalization to new environments.
Solution Approach 2:
The patent changes the parameter selection criteria by shifting from using all features to using only features with causal invariance. This parameter change in the feature selection process transforms the model's behavior from environment-specific to environment-agnostic, thereby improving reliability across different data distributions.
3Measurement precision
If causal invariance is enforced across different environments, then prediction accuracy in new environments is improved, but the complexity of the data processing method increases
Solution Approach 1:
The patent performs preliminary causal discovery and invariance detection during the offline training phase, before deployment to new environments. By pre-identifying and selecting features with causal invariance in advance, the system avoids the need for complex real-time analysis during deployment, thus reducing online computational complexity while maintaining high prediction accuracy.
Solution Approach 2:
The system uses automated causal discovery algorithms and invariance detection mechanisms that self-identify the invariant features without requiring manual intervention or domain expertise. This self-service approach to feature selection reduces the complexity burden on users while achieving the goal of causal invariance.
Data Source
AI summary
Embodiments of the present disclosure relate to method, device and computer-readable storage medium for data processing. A method for data processing comprises obtaining user data of a target user under a target environment. The user data comprises observational data of a plurality of features of the target user. The method further comprises extracting at least part of user data from the user data. The at least part of user data comprises observational data of at least one feature of the plurality of features which affects a target feature and has causal invariance. The method further comprises generating, based on the at least part of user data and a prediction model trained for the at least one feature, a prediction result for the target feature of the target user. The embodiments of the present disclosure further provide a device and a computer-readable storage medium that can perform the above method. The embodiments of the present disclosure can accurately and robustly make predictions based on features with causal invariance.