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 …

As real-world data (RWD) become more readily available, the regulatory agencies, medical
product developers, and other key stakeholders have increasing interests in exploring the …

This outlook paper reviews the research of van der Laan's group on Targeted Learning, a
subfield of statistics that is concerned with the construction of data adaptive estimators of …

Consider a study in which one observes n independent and identically distributed random
variables whose probability distribution is known to be an element of a particular statistical …

The objective of this chapter is to describe how the TMLE framework can be generalized to
explicitly utilize higher-order rather than first-order asymptotic representations. The practical …

About forty years ago, in a now–seminal contribution, Rosenbaum and Rubin (1983)
introduced a critical characterization of the propensity score as a central quantity for drawing …

Causal inference generally requires making some assumptions on a causal mechanism
followed by statistical estimation. The statistical estimation problem in causal inference is …

The types of research questions we face in medicine, technology, and business continue to
increase in their complexity with our growing ability to obtain novel forms of data. Much of …

In randomized trials, adjustment for measured covariates during the analysis can reduce
variance and increase power. To avoid misleading inference, the analysis plan must be pre …

Monitoring mechanisms under the United Nations (UN) core human rights treaties include
state reporting, inter-state communications, individual communications, inquiries, and …