This chapter examines the Elimination of Alternative Explanations (EAE) as a tool to bolster causal inference in impact evaluation, especially in complex educational contexts where creating pure counterfactuals is difficult. It links EAE to counterfactual analysis by outlining its origins, definition, and assumptions. EAE originated as a critique of overconfidence in counterfactual reasoning, emphasizing ruling out rival hypotheses. The chapter discusses principles like identifying confounders, controlling variables, designing comparisons, ensuring validity, and contextualizing results. It reviews methods such as ANCOVA, quasi-experimental, theory-based, and mixed approaches to enhance causal plausibility. A case study of technological high schools in Santa Catarina illustrates EAE’s use, showing how high school attendance affects income and mobility beyond a single school. It also covers challenges like unobserved factors, data issues, ethical concerns, and generalizability. Combining theory, methods, and examples, the chapter presents EAE as a practical complement to counterfactual analysis, particularly in educational and social research where experiments are often unfeasible.