no code implementations • 15 Aug 2023 • Shuyan Wang
The goal of Causal Discovery is to find automated search methods for learning causal structures from observational data.
no code implementations • 3 Jul 2021 • Shuyan Wang, Peter Spirtes
Kalisch and B\"{u}hlmann (2007) showed that for linear Gaussian models, under the Causal Markov Assumption, the Strong Causal Faithfulness Assumption, and the assumption of causal sufficiency, the PC algorithm is a uniformly consistent estimator of the Markov Equivalence Class of the true causal DAG for linear Gaussian models; it follows from this that for the identifiable causal effects in the Markov Equivalence Class, there are uniformly consistent estimators of causal effects as well.
no code implementations • 18 Sep 2020 • Shuyan Wang
The tetrad constraint is a condition of which the satisfaction signals a rank reduction of a covariance submatrix and is used to design causal discovery algorithms that detects the existence of latent (unmeasured) variables, such as FOFC.
no code implementations • 2 Sep 2019 • Shuyan Wang
In many scientific contexts, different investigators experiment with or observe different variables with data from a domain in which the distinct variable sets might well be related.