(2022). Classical Strategies and Concepts in Causal Discovery. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-0
MLA
Classical Strategies and Concepts in Causal Discovery. The Simons Institute for the Theory of Computing, Jan. 19, 2022, https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-0
BibTex
@misc{ scivideos_18998,
doi = {},
url = {https://simons.berkeley.edu/talks/introduction-causal-discovery-methods-0},
author = {},
keywords = {},
language = {en},
title = {Classical Strategies and Concepts in Causal Discovery},
publisher = {The Simons Institute for the Theory of Computing},
year = {2022},
month = {jan},
note = {18998 see, \url{https://scivideos.org/Simons-Institute/18998}}
}
An overview of the classical strategies (constraint-based algorithms, score-based algorithms) in learning causal DAGs. Relevant graphical and statistical concepts will be discussed, including Markov equivalence, faithfulness, conditional independence testing, consistency of the BIC score for selection, and theoretical properties of methods such as the PC algorithm and GES algorithm.