Latest Articles for Counterfactuals

A study on the reliability of LLM self-explanations in natural language tasks.

X-Fake framework enhances the quality of simulated SAR images for better deep learning applications.

New technique for better understanding model predictions using counterfactual examples.

New approach improves counterfactual generation using causal representation and diffusion models.

A look at fairness in hiring, lending, and law enforcement.

This article explains how counterfactuals enhance clarity in machine learning decisions.

This research enhances information retrieval systems by focusing on non-relevance.

This paper explores methods to assess policy effects over time.

MoPaDi creates counterfactual images to aid cancer diagnostics.

Researchers enhance data generation through the study of Conditional Hierarchical Variational Autoencoders.

A fresh approach to generating meaningful explanations for time-series data analysis.

A new method improves understanding of clustering results through counterfactual explanations.

Introducing a flexible method to estimate causal effects over time using advanced techniques.

Mediffusion uses AI to classify and generate medical images effectively.

Using thought experiments to transform data visualization practices for better understanding.

CausCell transforms single-cell data analysis with clarity and precision.

Revolutionizing how we estimate treatment effects using interconnected data.