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Produktbild: The Data Analyst's Guide to Cause and Effect

The Data Analyst's Guide to Cause and Effect An Introduction to Causal Inference in Practice

Fr. 72.90

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.05.2026

Verlag

Sage Publications

Seitenzahl

168

Maße (L/B/H)

21.4/13.6/1.1 cm

Gewicht

202 g

Sprache

Englisch

EAN

9798348848712

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.05.2026

Verlag

Sage Publications

Seitenzahl

168

Maße (L/B/H)

21.4/13.6/1.1 cm

Gewicht

202 g

Sprache

Englisch

EAN

9798348848712

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: The Data Analyst's Guide to Cause and Effect

  • About the Authors
    Series Editor's Introduction
    Acknowledgments
    Chapter 1: Introduction
    The fundamental promise of causal inference
    Causal inference is "EEESI"
    The R programming language
    Formal notation
    Chapter objectives
    Further reading
    Chapter 2: Causal Graphs
    Randomizing a DAG
    Elementary ingredients of DAGs
    Good and bad controls
    Where do DAGs come from?
    Average people and people on average
    Chapter objectives
    Further reading
    Chapter 3: G-methods and Marginal Effects
    Inverse probability weighting
    G-computation
    It's assumptions all the way down
    Chapter objectives
    Further reading
    Chapter 4: Adventures in G-methods
    Doubly robust estimation
    Sub-group analysis
    Complex longitudinal designs
    Mediation analysis: Crossing hypothetical worlds
    Chapter objectives
    Further reading
    Chapter 5: Most of Your Data is Almost Always Missing
    External validity and selection bias
    Poststratification
    The treatment effects zoo
    Target populations and econometrics
    Chapter objectives
    Further reading
    Chapter 6: More Missing Data
    To be or not to be missing
    Completely random terminology
    Missing data imputation
    Chapter objectives
    Further reading
    Chapter 7: Multilevel modelling and Mundlak's legacy
    Causal inference as counterfactual prediction
    Mundlak models
    Marginal effects in a multilevel model
    Chapter objectives
    Further reading
    Chapter 8: Causal Inference is not Easy
    Violations of identification assumptions and some solutions
    Bayesian causal modelling
    Perspectives on RCT data analysis
    Causal inference in the era of Big Data and AI
    Conclusion
    References
    Index