counterfactual explanations for XGBoost and tree ensemble models - counterfactual reasoning - model interpretability
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Updated
Jul 5, 2021 - C++
counterfactual explanations for XGBoost and tree ensemble models - counterfactual reasoning - model interpretability
Interpretation of Black-Box Model with Counterfactual with Whale optimization and Genetic Algorithm
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.
Implementation of the AAAI-2021 paper Sketch and Customize: A Counterfacutal Story Generator .
Parsing a resume, predicting whether it will get screened for a given job description or not and generating counterfactual suggestions.
Code for Diff-SCM paper
Official repository of the paper Face Presentation Attack Detection by Eacavating Causal Clues and Adapting Embedding Statistics
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
Python's pluggable backtester
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.
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers’ Decisions; preview paper at: https://arxiv.org/abs/2301.07941
Causal analysis for the Kappa language.
Easiest way to generate counterfactual explanations
This project aims to implement the Multi-task-Stacked-Bi-LSTMs applied in detecting the span of the counterfactual statement using ELMo Word Embedding and POS tags.
[EMNLP 2021] Table-based Fact Verification with Salience-aware Learning.
Counterfactual Graph Learning for Anomaly Detection on Attributed Networks, IEEE TKDE 2023
Summaries and notes on CounterFactual Machine Learning papers
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