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Use these libraries to find counterfactual models and implementations

Most implemented papers

Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

microsoft/DiCE 19 May 2019

Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.

Counterfactual Multi-Agent Policy Gradients

opendilab/DI-engine 24 May 2017

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 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 1 Nov 2017

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

carla-recourse/CARLA 22 Jul 2019

We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome.

Deep Counterfactual Regret Minimization

deepmind/open_spiel 1 Nov 2018

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

EricSteinberger/Deep-CFR 22 Jan 2019

Counterfactual Regret Minimization (CFR) is the most successful algorithm for finding approximate Nash equilibria in imperfect information games.

An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

mcgill-nlp/bias-bench 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

clinicalml/structuredinference 16 Nov 2015

Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.

Counterfactual Fairness

mkusner/counterfactual-fairness NeurIPS 2017

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.