Principles of Biostatistics

Abstract

to produce data that we can evaluate with a given method. Simulation can be used to rule out bad estimators and prove the value of good estimators to ourselves. Chapter 16, Fixed Effects, is about fixed effects, random effects, and fixed effects in the non-linear model and regression estimators. Fixed effects are methods of controlling for all variables whether they are observed or not, as long as they stay constant within some layer category. Chapter 17, Event Studies, is about event studies and how they work. The event study is probably the oldest and simplest causal inference research design. Event studies and performed in the stock market, event studies with regression, and event studies with multiple affected groups. Chapter 18, Differences-in-Differences, is about a method, a quasi-experimental approach that is concerned with comparing the changes in outcomes over time between a population enrolled in a program (the treatment group) and a population that is not (the comparison group). Its usefulness in data analysis has already been felt and appreciated. Chapter 19, Instrumental Variables, is about the working of instrumental variables and isolating variation instrumental variables designs seize directly on the concept of randomized control experiment. For work on instrumental variables, we must satisfy two assumptions: relevance of the instrument and validity of the instrument. Chapter 20, Regression Discontinuity, is about regression discontinuity. Regression discontinuity focuses on treatment that is assigned at a cutoff. It also focuses on the concept of running variable or forcing variable, cutoff, bandwidth, regression discontinuity with ordinary least squares, and the density discontinuity test. Chapter 21, A Gallery of Rogues: Other Methods, shows that the world of research design is too wide to be accommodated in a single book. There are methods and designs, both old and new, both tested and untested. This chapter demonstrates that there is a world of other methods that are new and yet developing. Among such exotic methods, the chapter talks about synthetic control, matrix completion, causal discovery, double machine learning, modeling of heterogeneous effects, causal forests, sorted effects, and structural estimation. This chapter could be inaccessible at the first reading, as the content of the chapter is either too advanced or too new to be grasped and understood. Chapter 22, Under the Rug, is all about the assumptions and concerns that are a part of pretty much any causal inference research study, but which often gets ignored or at least brushed aside. Overall, this book, though very voluminous, is an excellent addition to the world of literature. The book contains a good number of examples and wonderfully drawn diagrams, that facilitate a clearer understanding of the concepts. It is a wonderful exhibition of the parts and parcels of research design and causality.