Understanding Ceteris Paribus: Assumptions in Determining Causation

Discover how the ceteris paribus assumption isolates variables to clarify economic causation, simplifying complex ...
GitHub

kalinlau/awesome-causal-learning

BNN, BLR Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." 33rd International Conference on Machine Learning ...
GitHub

OpenASCE: A Python Package for End-to-End Large Scale Causal Learning

OpenASCE (Open All-Scale Casual Engine) is a comprehensive, easy-to-use, and efficient end-to-end large-scale causal learning system. It provides causal discovery, causal effect estimation, and ...
IEEE

Causal Meta-Transfer Learning for Cross-Domain Few-Shot Hyperspectral Image Classification

Abstract: Few-shot hyperspectral image (HSI) classification poses challenges due to sample selection bias in few-shot scenarios, potentially leading to incorrect statistical associations between ...
IEEE

Integrating Large Language Model for Improved Causal Discovery

Abstract: Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal ...