Algorithms for explaining machine learning models
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Updated
Jul 12, 2024 - Python
Algorithms for explaining machine learning models
CausalLift: Python package for causality-based Uplift Modeling in real-world business
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
A Python package for causal inference using Synthetic Controls
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
Counterfactual Samples Synthesizing for Robust VQA
(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models
Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
PyTorch implementation for our proposed CFIE in EMNLP 2021 paper "Uncovering Main Causalities for Long-tailed Information Extraction".
[ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR'.
Counterfactual Reasoning VQA Dataset
[CIKM 2023] Towards Fair Graph Neural Networks via Graph Counterfactual.
The corresponding code from our paper "Social Commonsense Reasoning with Multi-Head Knowledge Attention (EMNLP 2020)". Do not hesitate to open an issue if you run into any trouble!
EMNLP'22, CEM improves MHCH performance by correcting prediction bias and training an auxiliary cost simulator based on user state and labor cost causal graph, without requiring complex model crafting.
Official repository of the paper Face Presentation Attack Detection by Eacavating Causal Clues and Adapting Embedding Statistics
Easiest way to generate counterfactual explanations
[EMNLP 2021] Table-based Fact Verification with Salience-aware Learning.
Implementation of the AAAI-2021 paper Sketch and Customize: A Counterfacutal Story Generator .
Code for Diff-SCM paper
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