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Causal inference : the mixtape Scott Cunningham

By: Publication details: New Haven: Yale University Press, 2021.Description: x, 572 pages : illustrations ; 22 cmISBN:
  • 9780300251685 (Pbk)
  • 0300251688
Subject(s): DDC classification:
  • 300.72 C917C 23
  • 501 23
LOC classification:
  • Q175.32.C38 C86 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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Item type Current library Collection Call number Status Notes Date due Barcode
Books Books Central Library, IISER Bhopal On Display Reference 300.72 C917C (Browse shelf(Opens below)) Not For Loan Book recommended by Dr S. K. Agarwal 11687

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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