Counterfactual Explanation

56 papers with code • 0 benchmarks • 1 datasets

Returns a contrastive argument that permits to achieve the desired class, e.g., “to obtain this loan, you need XXX of annual revenue instead of the current YYY”


Most implemented papers

Counterfactual Explanation Algorithms for Behavioral and Textual Data

yramon/LimeCounterfactual 4 Dec 2019

This study aligns the recently proposed Linear Interpretable Model-agnostic Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of counterfactual explanations, and empirically benchmarks their effectiveness and efficiency against SEDC using a collection of 13 data sets.

Explaining Groups of Points in Low-Dimensional Representations

GDPlumb/ELDR ICML 2020

A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent.

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

indyfree/CARLA 2 Aug 2021

In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods.

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

microsoft/DiCE 10 Nov 2020

In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.

Counterfactual Explainable Recommendation

evison/Sentires 24 Aug 2021

Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.

Counterfactual Shapley Additive Explanations

jpmorganchase/cf-shap 27 Oct 2021

Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model.

On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations

marcovirgolin/robust-counterfactuals 22 Jan 2022

Since CEs typically prescribe a sparse form of intervention (i. e., only a subset of the features should be changed), we study the effect of addressing robustness separately for the features that are recommended to be changed and those that are not.

OmniXAI: A Library for Explainable AI

salesforce/omnixai 1 Jun 2022

We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice.

PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards

masoudhashemi/PermuteAttack 24 Aug 2020

We propose a model criticism and explanation framework based on adversarially generated counterfactual examples for tabular data.

Instance-based Counterfactual Explanations for Time Series Classification

e-delaney/Instance-Based_CFE_TSC 28 Sep 2020

In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data.