62 citations · 71 across the 4 of their papers we have counts for
4 papers
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…
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…
Identifying causal effects in maximally oriented partially directed acyclic graphs
Emilija Perković
We develop a necessary and sufficient causal identification criterion for maximally oriented partially directed acyclic graphs (MPDAGs). MPDAGs as a class of graphs include directe…
Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models
Leonard Henckel, Emilija Perković, Marloes H. Maathuis
Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, th…