1 code implementation • 26 Mar 2023 • Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome.
1 code implementation • 26 Mar 2023 • Tri Dung Duong, Qian Li, Guandong Xu
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).
no code implementations • 28 May 2021 • Tri Dung Duong, Qian Li, Guandong Xu
In our study, we advance the causal inference research by proposing a new effective framework to estimate the treatment effect on stochastic intervention.
no code implementations • 27 May 2021 • Tri Dung Duong, Qian Li, Guandong Xu
Central to these applications is the treatment effect estimation of intervention strategies.
1 code implementation • 3 May 2021 • Tri Dung Duong, Qian Li, Guandong Xu
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.
no code implementations • 27 Jun 2020 • Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, Xianzhi Wang
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 +2