Causal inference : the mixtape

• Main Author: Cunningham, Scott
• Format: eBook Book
• Language: English
• Published: New Haven Yale University Press 2021
• Edition: 1
• Subjects: BUSINESS & ECONOMICS / Econometrics; BUSINESS & ECONOMICS / Econometrics. bisacsh; Causation; Causation. fast (OCoLC)fst00849829; Data processing; Datenaufbereitung; Dependence (Statistics); Dependence (Statistics) fast (OCoLC)fst00890813; Induktive Statistik; Inference; Inference. fast (OCoLC)fst00972355; Kausalanalyse; Kausalität; Korrelation; Methodologie; Methodology; Schätztheorie; Social sciences; Social sciences > Data processing; Social sciences > Data processing. fast (OCoLC)fst01122901; Social sciences > Methodology; Social sciences > Methodology. fast (OCoLC)fst01122933; Sozialwissenschaften; Statistische Analyse; Statistische Methoden; Ökonometrie
• ISBN: 0300251688; 9780300251685
• DOI: 10.12987/9780300255881

An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists-who generally can't run controlled experiments to test and validate their hypotheses-apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages.