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466 Targeted learningods for nonparametric or semi-parametric statistical models, with a focus onmodels for censored data (van der Laan, 1996). Specifically, I worked on theconstruction of a semi-parametric efficient estimator of the bivariate survivalfunction based on bivariate right-censored failure time data, generalizing theKaplan–Meier estimator of a univariate survival function. At that time thebook by Bickel et al. (1997) on semi-parametric models was about to appearand earlier versions were circulating. There was an enormous interest amongthe theoreticians, and I had the fortune to learn from various inspiring intellectualleaders such as Richard Gill, Aad van der Vaart, Sara van de Geer,Peter Bickel, Jon Wellner, Richard Dudley, David Pollard, James Robins, andmany more.In order to deal with the challenges of these semi-parametric models I hadto learn about efficiency theory for semi-parametric models, relying on a socalled least favorable parametric submodel for which estimation of the desiredfinite dimensional estimand is as hard as it is in the actual infinite-dimensionalsemi-parametric model. I also had to compute projections in Hilbert spacesto calculate efficient influence curves and corresponding least favorable submodels.Richard Gill taught me how to represent an estimator as a functionalapplied to the empirical distribution of the data, and how to establish functionaldifferentiability of these estimator-functionals. I was taught about thefunctional delta-method which translates a) the convergence in distributionof the plugged in empirical process, and b) the functional differentiability ofthe estimator into the convergence in distribution of the standardized estimatorto a Gaussian process; see, e.g., Gill et al. (1995). I learned how tocompute the influence curve of a given estimator and that it is the objectthat identifies the asymptotic Gaussian process of the standardized estimators.In addition, Aad van der Vaart taught me about weak convergence ofempirical processes indexed by a class of functions, and Donsker classes definedby entropy integral conditions (van der Vaart and Wellner, 1996), whileRichard Gill taught me about models for the intensity of counting processesand continuous time martingales (Andersen et al., 1993). Right after my PhDthesis Jamie Robins taught me over the years a variety of clever methods forcalculating efficient influence curves in complex statistical models for complexlongitudinal data structures, general estimating equation methodologyfor semi-parametric models, and I learned about causal inference for multipletime-point interventions (Robins and Rotnitzky, 1992; van der Laan andRobins, 2003).At that time, I did not know about a large statistical community thatwould have a hard time accepting the formulation of the statistical estimationproblem in terms of a true statistical semi-parametric model, and anestimand/target parameter as a functional from this statistical model to theparameter space, as the way forward, but instead used quotes such as “Allmodels are wrong, but some are useful” to justify the application of wrongparametric models for analyzing data. By going this route, this communitydoes not only accept seriously biased methods for analyzing data in which