Defining the Model and Parameter

Abstract

Targeted statistical learning from data is often concerned with the estimation of causal effects and an assessment of uncertainty for the estimator. In Chap. 1, we identified the road map we will follow to solve this estimation problem. Now, we formalize the concepts of the model and target parameter. We will introduce additional topics that may seem abstract. While we attempt to elucidate these abstractions with tangible examples, depending on your background, the material may be quite dense compared to other textbooks you have read. Do not get discouraged. Sometimes a second reading and careful notes are helpful and sufficient to illuminate these concepts. Researchers and students at UC Berkeley have also had great success discussing these topics in groups. If this is your assigned text for a course or workshop, meet outside of class with your fellow classmates. We guarantee you that the effort is worth it so you can move on to the next step in the targeted learning road map. Once you have a firm understanding of the core material in Chap. 2, you can begin the estimation steps.