Causal inference : the mixtape / Scott Cunningham.

By:

Cunningham, Scott

Material type: Text

TextLanguage: English Publication details: London : Yale University Press, c2021.Description: x, 572 p. : ill. ; 22 cmISBN:

9780300251685
0300251688

Subject(s):

DDC classification:

300.72 23
501 23

LOC classification:

Q175.32 .C38C86 2021

Summary: 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. - -

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Holdings
Item type	Current library	Collection	Shelving location	Call number	Copy number	Status	Date due	Barcode
Books	North South University Library	Non-fiction	General Stacks	Q175.32.C38C86 2021 (Browse shelf(Opens below))	1	Available		49006
Books	North South University Library	Non-fiction	General Stacks	Q175.32.C38C86 2021 (Browse shelf(Opens below))	2	Checked out	2025-04-30	49007

Includes bibliographical references (pages 541-553) and index.

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. - -

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Causal inference : the mixtape / Scott Cunningham.