Algorithms for explaining machine learning models
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Updated
May 23, 2024 - Python
Algorithms for explaining machine learning models
Must-read papers and resources related to causal inference and machine (deep) learning
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
A Python package for causal inference using Synthetic Controls
Home for all packages related to the Counterfactual project
CausalLift: Python package for causality-based Uplift Modeling in real-world business
💡 Adversarial attacks on explanations and how to defend them
Counterfactual Samples Synthesizing for Robust VQA
Materials Collection for Causal Inference
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
📄 Counterfactual: Generalized State Channels Paper
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
MixEth: efficient, trustless coin mixing service for Ethereum
2nd place solution for Criteo Ad Placement challenge
(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models
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!
Generate dynamic structural causal models from biological knowledge graphs encoded in the Biological Expression Language (BEL)
Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
[ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR'.
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