Temporal data representing chronological observations of complex systems can be
ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many …

As machine learning is increasingly used to inform consequential decision-making (eg, pre-
trial bail and loan approval), it becomes important to explain how the system arrived at its …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

Learning a causal directed acyclic graph from data is a challenging task that involves
solving a combinatorial problem for which the solution is not always identifiable. A new line …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

We consider the problem of predicting a response variable from a set of covariates on a data
set that differs in distribution from the training data. Causal parameters are optimal in terms …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

The investigation of directed acyclic graphs (DAGs) encoding the same Markov property,
that is the same conditional independence relations of multivariate observational …

Algorithmic recourse seeks to provide actionable recommendations for individuals to
overcome unfavorable classification outcomes from automated decision-making systems …