Ema Perkovic (she/her)

Assistant Professor, University of Washington


Email perkovic@uw.edu
Phone +1 206 616-7430
UW Box Number 354322
Homepage Personal Home Page  
ORCID iD  0000-0003-3198-9213 

Emilija Perkovic joined the Department of Statistics at the University of Washington in Autumn 2018 as an Acting Assistant Professor and was promoted to a tenure-track Assistant Professor role in Autumn 2020.  Before coming to UW, she completed a Ph.D. in Statistics at ETH Zürich in 2018 under the supervision of Professor Marloes Maathuis, an M.Sc. in Statistics from ETH Zürich in 2014, and a B.Sc. in Mathematics from the University of Belgrade in 2012.  Her research interests are focused on causal inference from the perspective of graphical models. A large part of her Ph.D. thesis was on causal inference through covariate adjustment. She hopes to learn some new perspectives on causal inference while she is here.

Preprints

Variable elimination, graph reduction and efficient g-formula
F. Richard Guo, Emilija Perkovic, 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…

Efficient least squares for estimating total effects under linearity and causal sufficiency
F. Richard Guo, Emilija Perkovic
Recursive linear structural equation models are widely used to postulate causal mechanisms underlying observational data. In these models, each variable equals…

Minimal enumeration of all possible total effects in a Markov equivalence class
F. Richard Guo, Emilija Perkovic
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…

Variable elimination, graph reduction and efficient g-formula
F. Richard Guo, Emilija Perkovic, 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…

Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models
Leonard Henckel, Emilija Perkovic, 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…

Identifying causal effects in maximally oriented partially directed acyclic graphs
Emilija Perkovic
We develop a necessary and sufficient causal identification criterion for maximally oriented partially directed acyclic graphs (MPDAGs). MPDAGs as a class of…

Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
Emilija Perkovic, Johannes Textor, Markus Kalisch, Marloes H. Maathuis
We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic…

Interpreting and using CPDAGs with background knowledge
Emilija Perkovic, Markus Kalisch, Maloes H. Maathuis
We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing…

A Complete Generalized Adjustment Criterion
Emilija Perkovic, Johannes Textor, Markus Kalisch, Marloes H. Maathuis
Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in…