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M. van der Laan 467confidence intervals and p-values have no real meaning, but it also obstructsprogress by not formulating the true statistical challenge that needs to beaddressed to solve the actual estimation problem. In particular, due to thisresistance, we still see that most graduate students in biostatistics and statisticsprograms do not know much about the above mentioned topics, such asefficiency theory, influence curves, and efficient influence curves. I would nothave predicted at that time that I would be able to inspire new generationswith the very topics I learned in the 1990s.Throughout my career, my only goal has been to advance my understandingof how to formulate and address the actual estimation problems in a largevariety of real world applications, often stumbling on the need for new theoreticaland methodological advances. This has been my journey that startedwith my PhD thesis and is a product of being part of such a rich communityof scientists and young dynamic researchers that care about truth and standfor progress. I try and hope to inspire next generations to walk such journeys,each person in their own individual manner fully utilizing their individualtalents and skills, since it is a path which gives much joy and growth, andthereby satisfaction.In the following sections, I will try to describe my highlights of this scientificjourney, resulting in a formulation of the field targeted learning (van derLaan and Rose, 2012), and an evolving roadmap for targeted learning (Pearl,2009; Petersen and van der Laan, 2012; van der Laan and Rose, 2012) dealingwith past, current and future challenges that require the input for many generationsto come. To do this in a reasonably effective way we start out withproviding some succinct definitions of key statistical concepts such as statisticalmodel, model, target quantity, statistical target parameter, and asymptoticlinearity of estimators. Subsequently, we will delve into the constructionof finite sample robust, asymptotically efficient substitution estimators in realisticsemi-parametric models for experiments that generate complex highdimensional data structures that are representative of the current flood of informationgenerated by our society. These estimators of specified estimandsutilize the state of the art in machine learning and data adaptive estimation,while preserving statistical inference. We refer to the field that is concernedwith construction of such targeted estimators and corresponding statisticalinference as targeted learning.40.2 The statistical estimation problem40.2.1 Statistical modelThe statistical model encodes known restrictions on the probability distributionof the data, and thus represents a set of statistical assumptions. Let’s