\(\newcommand{\bbO}{\mathbb{O}}\) \(\newcommand{\bbD}{\mathbb{D}}\) \(\newcommand{\bbP}{\mathbb{P}}\) \(\newcommand{\bbR}{\mathbb{R}}\) \(\newcommand{\Algo}{\widehat{\mathcal{A}}}\) \(\newcommand{\Algora}{\widetilde{\mathcal{A}}}\) \(\newcommand{\calF}{\mathcal{F}}\) \(\newcommand{\calM}{\mathcal{M}}\) \(\newcommand{\calP}{\mathcal{P}}\) \(\newcommand{\calO}{\mathcal{O}}\) \(\newcommand{\calQ}{\mathcal{Q}}\) \(\newcommand{\defq}{\doteq}\) \(\newcommand{\Exp}{\textrm{E}}\) \(\newcommand{\IC}{\textrm{IC}}\) \(\newcommand{\Gbar}{\bar{G}}\) \(\newcommand{\one}{\textbf{1}}\) \(\newcommand{\psinos}{\psi_{n}^{\textrm{os}}}\) \(\renewcommand{\Pr}{\textrm{Pr}}\) \(\newcommand{\Phat}{P^{\circ}}\) \(\newcommand{\Psihat}{\widehat{\Psi}}\) \(\newcommand{\Qbar}{\bar{Q}}\) \(\newcommand{\tcg}[1]{\textcolor{olive}{#1}}\) \(\DeclareMathOperator{\Dirac}{Dirac}\) \(\DeclareMathOperator{\expit}{expit}\) \(\DeclareMathOperator{\logit}{logit}\) \(\DeclareMathOperator{\Rem}{Rem}\) \(\DeclareMathOperator{\Var}{Var}\)


This is either the website or the text called “A Ride in Targeted Learning Territory”. In the former case, the text can be dowloaded by clicking on the dedicated button in the top part of the webpage. In the latter case, the website can be browsed at https://achambaz.github.io/tlride/.

Long Mendocino Drive (detail, Liana Steinmetz)


The text takes the form of a series of brief sections. The main sections combine theoretical and computational developments. The code is written in the programming language R (R Core Team 2019). R is widely used among statisticians and data scientists to develop statistical software and data analysis.

Regularly, a section is inserted that proposes exercises. Each such section is indicated by the symbol. The symbol also indicates those sections of which the content is more involved.


After a short introduction, we present the reproducible experiment that will play a central role throughout the text. Then, we introduce the main parameter of interest. We comment upon some of its properties that are useful from a statistical perspective. This paves the way to the presentation of several estimators that are increasingly more powerful statistically. The discussion takes us into targeted learning territory.


The text might be of interest to students in statistics and machine learning. It might also serve as a gentle introduction to targeted learning in both its theoretical and computational aspects before delving deeper into the literature (Laan and Rose 2011), (Laan and Rose 2018).

The text was presented at the Journées d’Étude en Statistique 2018 held in Fréjus (France) in October 2018 and at the First Summer School in Statistics and Data Science for Young Researchers from French-speaking Africa held at AIMS Senegal (MBour, Senegal) in July 2019. The text and a shorter version (in French) will be published soon (Benkeser and Chambaz 2020a), (Benkeser and Chambaz 2020b).

Technical details

The text is written in RMarkdown with bookdown. Assuming that the knitr package is installed, you can retrieve all the R code by cloning this github repository then running