We provide a lay-language primer on the counterfactual model of causal inference and the logic of causal models. Topics include the representation of causal models with causal graphs and using causal graphs to read off relations of conditional independence among variables in a causal domain.

In Chapter 10 the analysis of longitudinal intervention studies is discussed. Most attention is given to the analysis of data from a randomised controlled trial (RCT). In light of the discussion, a distinction is made between an RCT with one follow-up measurement and an RCT with more than one follow-up measurement. For both situations, it is argued that a (longitudinal) analysis of covariance must be used for the analysis of RCT data. With an analysis of covariance, an adjustment is made for the baseline value of the outcome variable and therefore, an adjustment is made for regression to the mean. Besides the analysis of RCT data, the chapter also includes a discussion about stepped wedge trials and about the analysis of intervention effects in observational longitudinal studies. Finally, a discussion of the difference in difference method is provided. Also in this chapter, all methods are accompanied by extensive real- life data examples.

This chapter introduces key terms used to describe experiments and, more generally, the investigation of cause and effect. Because so many different disciplines use experiments, layers of overlapping terminology have accumulated, and this chapter tries to cut through the clutter by grouping synonyms, thereby keeping jargon to a minimum. In addition to providing definitions, this chapter explains why these key concepts are important in practice. The chapter starts with the basic ingredients of an experiment (treatments, outcomes). Next, we define what we mean by a causal effect, introducing the concept of potential outcomes. The chapter culminates in the presentation of three core assumptions for unbiased causal inference. These core assumptions figure prominently throughout the book, as readers are continually encouraged to assess whether illustrative experiments satisfy these assumptions in practice.

Issues of measuring policy impacts from observed data are discussed. Popular methods for controlling the bias induced by nonrandom samples and panel parametric, semi-parametric, and nonparametric methods are introduced. Conditions for simulating program impacts are discussed and examples are given.

A central problem in clinical psychology is how to draw statistical inferences about the causal effects of treatments (i.e., interventions) from randomized and nonrandomized data. For example, does exposure to childhood trauma really impair cognitive functioning in adults, or does a new medication for treating a psychological condition reduce the occurrence of negative physical health outcomes relative to existing treatments? This chapter describes a general approach to the estimation of such causal effects based on the Rubin Causal Model (RCM). Under this framework, causal effects are defined in terms of potential outcomes and inferences are based on a probabilistic assignment mechanism, which mathematically describes how treatments are given to units. Frequentist and model-based forms of statistical inference for causal effects in randomized experiments and observational studies are presented, and the application of this approach to a number of data collection designs and associated problems commonly encountered in clinical research is discussed.