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Found 6 papers, 3 papers with code
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome.
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i. e., what if the individual belongs to other sensitive groups).
Stochastic Intervention for Causal Inference via Reinforcement Learning
In our study, we advance the causal inference research by proposing a new effective framework to estimate the treatment effect on stochastic intervention.
Stochastic Intervention for Causal Effect Estimation
Central to these applications is the treatment effect estimation of intervention strategies.
Causality-based Counterfactual Explanation for Classification Models
Accordingly, the gradient-free methods are proposed to handle the categorical variables, which however have several major limitations: 1) causal relationships among features are typically ignored when generating the counterfactuals, possibly resulting in impractical guidelines for decision-makers; 2) the counterfactual explanation algorithm requires a great deal of effort into parameter tuning for dertermining the optimal weight for each loss functions which must be conducted repeatedly for different datasets and settings.
Causality Learning: A New Perspective for Interpretable Machine Learning
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc.
BIG-bench Machine Learning
Interpretable Machine Learning
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