This chapter provides an overview of the methodological and practical issues that arise when estimating causal relationships of interest to labor economists. It covers identification, data collection, and measurement problems. Four identification strategies are discussed, and five empirical examples—effects of schooling, unions, immigration, military service, and class size—illustrate the methodological points. The chapter adopts an experimentalist perspective, distinguishing between variables with causal effects, control variables, and outcome variables. It also discusses secondary data sets, primary data collection strategies, and administrative data. The section on measurement issues focuses on recent empirical examples, presents a summary of empirical findings on the reliability of key labor market data, and briefly reviews the role of survey sampling weights and the allocation of missing values in empirical research. The chapter begins by discussing the importance of empirical analysis in labor economics, noting that it relies on diverse data sources. It highlights the increasing use of primary data collection and the growing reliance on a wide range of data sets. The chapter then discusses identification strategies for causal relationships, including control for confounding variables, fixed-effects and differences-in-differences, instrumental variables, and regression-discontinuity designs. It also addresses the consequences of heterogeneity and nonlinearity, refutability, and data collection strategies. The chapter emphasizes the challenges of identifying causal relationships in labor economics, particularly the difficulty of isolating the effects of specific variables from other factors. It discusses the use of quasi-experimental methods, which rely on economic theory to frame causal questions and identify causal effects. The chapter also addresses measurement issues, including the impact of measurement error and the role of survey sampling weights and missing values in empirical research. The chapter concludes by summarizing the key findings and emphasizing the importance of empirical strategies in labor economics. It highlights the need for careful consideration of identification strategies, data collection methods, and measurement issues to ensure the validity and reliability of empirical findings in labor economics.This chapter provides an overview of the methodological and practical issues that arise when estimating causal relationships of interest to labor economists. It covers identification, data collection, and measurement problems. Four identification strategies are discussed, and five empirical examples—effects of schooling, unions, immigration, military service, and class size—illustrate the methodological points. The chapter adopts an experimentalist perspective, distinguishing between variables with causal effects, control variables, and outcome variables. It also discusses secondary data sets, primary data collection strategies, and administrative data. The section on measurement issues focuses on recent empirical examples, presents a summary of empirical findings on the reliability of key labor market data, and briefly reviews the role of survey sampling weights and the allocation of missing values in empirical research. The chapter begins by discussing the importance of empirical analysis in labor economics, noting that it relies on diverse data sources. It highlights the increasing use of primary data collection and the growing reliance on a wide range of data sets. The chapter then discusses identification strategies for causal relationships, including control for confounding variables, fixed-effects and differences-in-differences, instrumental variables, and regression-discontinuity designs. It also addresses the consequences of heterogeneity and nonlinearity, refutability, and data collection strategies. The chapter emphasizes the challenges of identifying causal relationships in labor economics, particularly the difficulty of isolating the effects of specific variables from other factors. It discusses the use of quasi-experimental methods, which rely on economic theory to frame causal questions and identify causal effects. The chapter also addresses measurement issues, including the impact of measurement error and the role of survey sampling weights and missing values in empirical research. The chapter concludes by summarizing the key findings and emphasizing the importance of empirical strategies in labor economics. It highlights the need for careful consideration of identification strategies, data collection methods, and measurement issues to ensure the validity and reliability of empirical findings in labor economics.