Python's pluggable backtester
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Updated
May 29, 2024 - Python
Python's pluggable backtester
Algorithms for explaining machine learning models
(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models
Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
💡 Adversarial attacks on explanations and how to defend them
[CIKM 2023] Towards Fair Graph Neural Networks via Graph Counterfactual.
[ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR'.
A Python package for causal inference using Synthetic Controls
Parsing a resume, predicting whether it will get screened for a given job description or not and generating counterfactual suggestions.
Counterfactual Graph Learning for Anomaly Detection on Attributed Networks, IEEE TKDE 2023
The effectiveness of individual NPI among the 42 states in the US where death count exceeded 100 during the first wave of the COVID-19 pandemic
cfid: R package for identifying counterfactuals.
Counterfactual Reasoning VQA Dataset
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Easiest way to generate counterfactual explanations
Official repository of the paper Face Presentation Attack Detection by Eacavating Causal Clues and Adapting Embedding Statistics
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
The project is about providing counterfactual explanations for biased recommendation results. The developed algorithms produce explanations efficiently for the case of random walk-based recommenders. The scenario tested in the experiments is user-movie ratings, but the algorithms can be applied in any user-item interactions scheme.
CausalLift: Python package for causality-based Uplift Modeling in real-world business
Materials Collection for Causal Inference
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