2 papers
stat.ML2021
A Subsampling-Based Method for Causal Discovery on Discrete Data
Austin Goddard, Yu Xiang
Inferring causal directions on discrete and categorical data is an important yet challenging problem. Even though the additive noise models (ANMs) approach can be adapted to the di…
stat.ME2020
Causal Inference from Slowly Varying Nonstationary Processes
Kang Du, Yu Xiang
Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data gen…