Algorithms for explaining machine learning models
-
Updated
Dec 3, 2024 - Python
Algorithms for explaining machine learning models
Must-read papers and resources related to causal inference and machine (deep) learning
CausalLift: Python package for causality-based Uplift Modeling in real-world business
💡 Adversarial attacks on explanations and how to defend them
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Home for all packages related to the Counterfactual project
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
FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
📄 Counterfactual: Generalized State Channels Paper
(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models
MixEth: efficient, trustless coin mixing service for Ethereum
Materials Collection for Causal Inference
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
Summaries and notes on CounterFactual Machine Learning papers
Add a description, image, and links to the counterfactual topic page so that developers can more easily learn about it.
To associate your repository with the counterfactual topic, visit your repo's landing page and select "manage topics."