Causal inference

• Verfasst von: Rosenbaum, Paul R. [VerfasserIn]
• Titel: Causal inference
• Verf.angabe: Paul R. Rosenbaum
• Verlagsort: Cambridge, Massachusetts ; London, England
• Verlag: The MIT Press
• E-Jahr: 2023
• Jahr: [2023]
• Umfang: 1 Online-Ressource (203 Seiten)
• Gesamttitel/Reihe: The MIT Press essential knowledge series
• Fussnoten: Description based on publisher supplied metadata and other sources
• ISBN: 978-0-262-37354-8
• ISBN: 978-0-262-37353-1
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Rosenbaum, Paul R.: Causal inference. - Cambridge, Massachusetts : The MIT Press, 2023. - 203 Seiten
• Sach-SW: Electronic books
• K10plus-PPN: 1847688527
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.

Abstract

Intro -- Contents -- Series Foreword -- List of Examples -- List of Methodological Topics -- 1: The Effects Caused by Treatments -- What Is a Causal Effect? Why Is Causal Inference Difficult? -- Notation for a Precise Question -- Would a Control Group Solve the Problem? -- It Is Just the Same with More Than Two People -- Flipping a Fair Coin to Assign Treatments to People -- The Average Treatment Effect (ATE) -- 2: Randomized Experiments -- The Effects of Treatments for Ebola Virus Disease -- A Randomized Controlled Trial -- Why Assign Treatments at Random? -- Randomized Treatment Assignment and Causal Inference -- Estimating the Average Treatment Effect -- Testing the Hypothesis of No Effect -- What Is Special about Coin Flips? -- Bleeding George Washington and the Theory of Humors -- 3: Observational Studies -- What Are Observational Studies? -- Smoking and Periodontal Disease -- Interlude: Tukey's Boxplot -- Do Treated and Control Groups Look Comparable in Boxplots? -- Comparing Outcomes in Groups That Are Not Comparable -- 4: Adjustments for Measured Covariates -- Matching for Covariates as a Method of Adjustment -- Imbalances in the Propensity Score -- How Matching for the Propensity Score Balances Measured Covariates -- Comparing Outcomes While Controlling Measured Covariates -- Other Matching Techniques -- 5: Sensitivity to Unmeasured Covariates -- Objections, Counterclaims, and Rival Hypotheses -- Smoking and Lung Cancer -- The First Sensitivity Analysis in an Observational Study -- Modern Sensitivity Analysis: Smoking and Periodontal Disease -- The Role of Sensitivity Analysis -- 6: Quasi-Experimental Devices in the Design of Observational Studies -- Anticipated Counterclaims -- Two Control Groups -- The Logic of Two Control Groups -- Untreated Counterparts in Addition to Untreated Controls -- Resolving Anticipated Counterclaims.