Proximal causal inference is a recently proposed framework for evaluatin...
We study experiment design for the unique identification of the causal g...
We consider the task of identifying and estimating the causal effect of ...
Proximal causal inference was recently proposed as a framework to identi...
Linear structural causal models (SCMs) – in which each observed variable...
Graphical causal models led to the development of complete non-parametri...
We consider the problem of learning the causal MAG of a system from
obse...
Machine learning algorithms are increasingly used for consequential deci...
In many applications, researchers are interested in the direct and indir...
A moment function is called doubly robust if it is comprised of two nuis...
We study the impact of pre and post processing for reducing discriminati...
One of the main approaches for causal structure learning is constraint-b...
Learning graphical structure based on Directed Acyclic Graphs (DAGs) is ...
Markov blanket feature selection, while theoretically optimal, generally...
The main way for defining equivalence among acyclic directed graphs is b...
It is known that from purely observational data, a causal DAG is identif...
We consider the problem of learning causal models from observational dat...
Modern real-time systems (RTS) are increasingly the focus of security
th...
We propose an exact solution for the problem of finding the size of a Ma...
Automated decision making systems are increasingly being used in real-wo...
We study the problem of causal structure learning when the experimenter ...
We study causal inference in a multi-environment setting, in which the
f...
In real-time embedded systems (RTS), failures due to security breaches c...
We study the problem of causal structure learning over a set of random
v...
Interaction information is one of the multivariate generalizations of mu...