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