Estimation of interventional effects of features on prediction
| Autor: | Shohei Shimizu, Patrick Blöbaum |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science Test data generation business.industry Mechanism (biology) Feature extraction Machine Learning (stat.ML) 02 engineering and technology Causal structure Machine learning computer.software_genre Data modeling Statistics - Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business computer Predictive modelling Interpretability |
| Zdroj: | MLSP |
| Popis: | The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the predictors and the actual prediction have not been considered. Here, we connect the underlying causal structure of a data generation process and the causal structure of a prediction mechanism. To achieve this, we propose a framework that identifies the feature with the greatest causal influence on the prediction and estimates the necessary causal intervention of a feature such that a desired prediction is obtained. The general concept of the framework has no restrictions regarding data linearity; however, we focus on an implementation for linear data here. The framework applicability is evaluated using artificial data and demonstrated using real-world data. To appear in Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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