Conducting valid statistical analyses is challenging in the presence of
...
Despite the growing interest in causal and statistical inference for set...
We implement Ananke: an object-oriented Python package for causal infere...
It is often said that the fundamental problem of causal inference is a
m...
The front-door criterion can be used to identify and compute causal effe...
Significant progress has been made in developing identification and
esti...
Establishing cause-effect relationships from observational data often re...
In classical causal inference, inferring cause-effect relations from dat...
Black box models in machine learning have demonstrated excellent predict...
Missing data has the potential to affect analyses conducted in all field...
The last decade witnessed the development of algorithms that completely ...
Recently there has been sustained interest in modifying prediction algor...
Missing data is a pervasive problem in data analyses, resulting in datas...
The goal of personalized decision making is to map a unit's characterist...
We consider the problem of learning optimal policies from observational ...
In this paper, we consider the problem of fair statistical inference
inv...