counterfactual
736 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in counterfactual
Libraries
Use these libraries to find counterfactual models and implementationsMost implemented papers
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
microsoft/DiCE
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Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
Counterfactual Multi-Agent Policy Gradients
COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.
Unbiased Scene Graph Generation from Biased Training
KaihuaTang/Scene-Graph-Benchmark.pytorch
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CVPR 2020
Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
microsoft/DiCE
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We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims.
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome.
Deep Counterfactual Regret Minimization
This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game.
Single Deep Counterfactual Regret Minimization
Counterfactual Regret Minimization (CFR) is the most successful algorithm for finding approximate Nash equilibria in imperfect information games.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
indyfree/CARLA
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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.
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
mcgill-nlp/bias-bench
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ACL 2022
Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on.
Deep Kalman Filters
Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.
