189 citations · 846 across the 31 of their papers we have counts for
3 papers · 1 filter
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Haoyue Dai, Peter Spirtes, Kun Zhang
Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables a…
The m-connecting imset and factorization for ADMG models
Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson +1
Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learn…
Causal discovery for observational sciences using supervised machine learning
Anne Helby Petersen, Joseph Ramsey, Claus Thorn Ekstrøm +1
Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorith…