{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":22,"n_cols":5,"rows_kept":22,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[15,2,15,3],[16,2,16,3],[19,2,19,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910554223803321"]
[null,null,"Autore",null,"Cunningham Scott"]
[null,null,"Titolo",null,"Causal inference : the mixtape / / Scott Cunningham"]
[null,null,"Pubbl/distr/stampa",null,"New Haven, Connecticut : , : Yale University Press, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"9780300255881"]
[{"$nulls":4},"0-300-25588-8"]
[{"$nulls":4},"9780300251685"]
[null,null,"Descrizione fisica",null,"1 online resource (352 pages) : illustrations"]
[null,null,"Disciplina",null,"501"]
[null,null,"Soggetti",null,"Causation"]
[{"$nulls":4},"Inference"]
[{"$nulls":4},"analysis of causes"]
[{"$nulls":4},"social sciences"]
[{"$nulls":4},"information analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"What Is Causal Inference? -- Do Not Confuse Correlation with Causality -- OptimizationMakes Everything Endogenous -- Example: Identifying Price Elasticity of Demand -- Conclusion -- Probability and Regression Review -- Directed Acyclic Graphs -- Introduction -- Introduction to DAG Notation -- Potential Outcomes Causal Model -- Introduction -- Physical Randomization -- Randomization Inference -- Conclusion -- Matching and Subclassification -- Subclassification -- Exact Matching -- Approximate Matching -- Regression Discontinuity -- Huge Popularity of Regression Discontinuity -- Estimation Using an RDD -- Challenges to Identification -- Replicating a Popular Design: The Close Election -- Regression Kink Design -- Conclusion -- Instrumental Variables -- History of Instrumental Variables: Father and Son -- Intuition of Instrumental Variables -- Homogeneous Treatment Effects -- Parental Methamphetamine Abuse and Foster Care -- The Problem of Weak Instruments -- Heterogeneous Treatment Effects -- Applications -- Popular IV Designs -- Conclusion -- Panel Data -- DAG Example -- Estimation -- Data Exercise: Survey of Adult Service Providers -- Conclusion -- Difference-in-Differences -- John Snow’s Cholera Hypothesis -- Estimation -- Inference -- Providing Evidence "]
[{"$nulls":4},"for Parallel Trends Through Event Studies and Parallel Leads -- The Importance of Placebos in DD -- Twoway Fixed Effects with Differential Timing -- Conclusion -- Synthetic Control -- Introducing the Comparative Case Study -- Prison Construction and Black Male Incarceration -- Conclusion."]
[null,null,null,"Sommario/riassunto","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."]