This chapter focuses on causal inference in healthcare, emphasizing the need to identify causal relationships in data to answer important questions related to efficacy, mortality, productivity, and care delivery models. The authors discuss the limitations of randomized controlled trials due to ethical or pragmatic considerations and introduce quasi-experimental research designs as a scientifically coherent alternative. They divide these designs into two broad categories, independence-based designs and model-based designs, and explain the validity of assumptions necessary for each design. The chapter covers key concepts such as potential outcomes, selection bias, heterogeneous treatment effects bias, average treatment effect, average treatment effect for the treated and untreated, and local average treatment effect. Additionally, it discusses important quasi-experimental designs such as regression discontinuity, difference-in-differences, and synthetic controls. The chapter concludes by highlighting the importance of careful selection and application of these methods to estimate causal effects accurately and open the black box of healthcare.

This chapter addresses the often-misunderstood concept of validity. Much of the methodological discussion around sociological experiments is framed in terms of internal and external validity. The standard view is that the more we ensure that the experimental treatment is isolated from potential confounds (internal validity), the more unlikely it is that the experimental results can be representative of phenomena of the outside world (external validity). However, other accounts describe internal validity as a prerequisite of external validity: Unless we ensure internal validity of an experiment, little can be said of the outside world. We contend in this chapter that problems of either external or internal validity do not necessarily depend on the artificiality of experimental settings or on the laboratory–field distinction between experimental designs. We discuss the internal–external distinction and propose instead a list of potential threats to the validity of experiments that includes "usual suspects" like selection, history, attrition, and experimenter demand effects and elaborate on how these threats can be productively handled in experimental work. Moreover, in light of the different types of experiments, we also discuss the strengths and weaknesses of each regarding threats to internal and external validity.

As a continuation of the overall introduction to the book, Chapter 2 summarizes the main contributions of economics to understanding the role of education in society and to educational policy. The chapter details these contributions in three parts: (1) economists have demonstrated that education has an important economic dimension (that it has economic value), and they have inserted education policy near the center of the debate on economic development and material well-being; (2) they have formalized concrete models of student learning, both in and out of school, and have developed models of educational production, in which schools, districts, and states are economic decision units, allocating resources to produce educational outputs – and where incentives and resource allocation decisions affect the productivity of teachers and student learning, economists have been able to model a number of strategies that increase output and test them empirically; and (3) economists have also been at the forefront of applying new and increasingly sophisticated statistical techniques to estimate quantitatively the causal effects of various educational policies on student academic outcomes and adult economic and social outcomes.