8 citations · 28 across the 10 of their papers we have counts for
5 papers · 1 filter
Variable elimination, graph reduction and efficient g-formula
F. Richard Guo, Emilija Perković, Andrea Rotnitzky
We study efficient estimation of an interventional mean associated with a point exposure treatment under a causal graphical model represented by a directed acyclic graph without hi…
Minimal enumeration of all possible total effects in a Markov equivalence class
F. Richard Guo, Emilija Perković
In observational studies, when a total causal effect of interest is not identified, the set of all possible effects can be reported instead. This typically occurs when the underlyi…
Efficient least squares for estimating total effects under linearity and causal sufficiency
F. Richard Guo, Emilija Perković
Recursive linear structural equation models are widely used to postulate causal mechanisms underlying observational data. In these models, each variable equals a linear combination…
Chernoff-type Concentration of Empirical Probabilities in Relative Entropy
F. Richard Guo, Thomas S. Richardson
We study the relative entropy of the empirical probability vector with respect to the true probability vector in multinomial sampling of categories, which, when multiplied by s…
On Testing Marginal versus Conditional Independence
F. Richard Guo, Thomas S. Richardson
We consider testing marginal independence versus conditional independence in a trivariate Gaussian setting. The two models are non-nested and their intersection is a union of two m…