Chapters

Chapter 1 — Introduction Experimentation is the sharpest tool available for solving the causal inference problem. This chapter frames the book around two Experimental Problems: EP1—quantifying economic fundamentals, measuring treatment effects, and identifying key mediators and moderators in an ethically...

Chapter 2 — A Primer on Economic Experiments What makes an experiment “economic,” and what distinguishes a lab from a field? This chapter maps the full experimental spectrum—from conventional laboratory experiments to artefactual, framed, and natural field experiments—explaining what parameters...

Chapter 3 — Internal Validity: Identification in Economic Experiments At the heart of experimental economics is the potential outcomes framework and the challenge of recovering causal effects from data where only one state of the world is ever observed. This...

Chapter 4 — Statistical Conclusion Validity: Measurement in Economic Experiments Recovering a causal effect is only half the battle—measuring it precisely and drawing valid inferences is the other. This chapter covers the superpopulation and finite-population frameworks for estimating average treatment...

Chapter 5 — Optimal Experimental Design Good experiments don’t just happen—they are engineered. This chapter provides a rigorous framework for calculating optimal sample sizes, trading off statistical power against cost, and making design choices that maximize the likelihood of detecting...

Chapter 6 — Randomization Techniques Randomization is the engine of causal inference, but “how to randomize” is less obvious than it sounds. This chapter surveys the landscape of classical assignment mechanisms—Bernoulli trials, completely randomized experiments, randomized block (stratified) experiments, rerandomization,...

Chapter 7 — Heterogeneity and Causal Moderation Average treatment effects tell you what happened on average—but heterogeneity reveals who was affected, how much, and why. This chapter develops tools for estimating conditional average treatment effects (CATEs) using both classical regression...

Chapter 8 — Mediation: Exploring Relevant Mechanisms Knowing that a treatment works is useful. Knowing why it works is transformative. This chapter develops mediation analysis as the structured approach to uncovering causal pathways between treatment and outcome. It introduces the...

Chapter 9 — Experiments with Longitudinal Elements Many economic questions involve outcomes that unfold over time—requiring experimental designs that account for treatment duration, timing, and repeated exposure. This chapter develops the potential outcomes framework for longitudinal settings, covering staggered experimental...

Chapter 10 — Within-Subject Experimental Designs In a between-subject design, different units are assigned to treatment and control. In a within-subject design, the same unit is observed in both—a powerful but demanding approach. This chapter develops the potential outcomes framework...

Chapter 11 — SUTVA: Interference and Hidden Treatments The Stable Unit Treatment Value Assumption underlies every standard estimate of the average treatment effect. This chapter examines what happens when it fails—either because of spillovers between units (interference) or because the...

Chapter 12 — Observability: Nonrandom Attrition Missing outcome data is one of the most common and most damaging threats to experimental integrity. This chapter develops the implications of nonrandom attrition within the potential outcomes framework, covering tests for selective attrition...

Chapter 13 — Complete Compliance: One-Sided and Two-Sided Violations When subjects don’t take the treatment they were assigned—or take treatment when assigned to control—the experiment faces a compliance problem. This chapter distinguishes one-sided from two-sided noncompliance, shows why imperfect compliance...

Chapter 14 — Statistical Independence and Compromised Randomization Randomization is supposed to ensure that treatment assignment is independent of potential outcomes—but what happens when something goes wrong? This chapter addresses two scenarios: when the experimenter controls the assignment mechanism but...

Chapter 15 — Building Confidence in (and Knowledge from) Experimental Results A single finding is a data point. A body of reliable, replicated evidence is scientific knowledge. This chapter examines the post-study probability that a research finding is true, the...

Chapter 16 — Generalizability and Scaling Internal validity is a prerequisite, not a destination. This chapter tackles Experimental Problem 2: whether causal effects found in one environment transfer to others. It develops the assumptions required for external validity—external unconfoundedness, overlap,...

Chapter 17 — The Ethics of Economic Experiments Rigorous design is not enough—research must also be conducted responsibly. This chapter develops an economic model of the trade-offs researchers face when their interests conflict with those of subjects or innocent bystanders,...

Chapter 18 — Pre-treatment Administrative Responsibilities Before a single subject is treated, the experimenter faces a checklist of administrative obligations that determine whether the study will be feasible, ethical, and credible. This chapter walks through the full pre-treatment sequence: IRB...

Chapter 19 — Optimal Use of Incentives in Economic Experiments Every design decision is ultimately an incentive decision. This chapter develops a unified economic model of experimental resource allocation, showing that the optimal design equalizes the marginal benefit of the...

Chapter 20 — Epilogue: The (Written) Road to Scientific Knowledge Diffusion Executing science creates evidence. Writing science diffuses it. This epilogue guides the researcher through the final step of the scientific process: communicating findings to the scholarly community in a...