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020			_a9780300251685
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040			_aYDX _cBD-DhNSU _dBD-DhNSU
041			_aeng
050	0	0	_aQ175.32 _b.C38C86 2021
082	0	4	_a300.72 _223
082	0	4	_a501 _223
100	1		_aCunningham, Scott,
245	1	0	_aCausal inference : _bthe mixtape / _cScott Cunningham.
260			_aLondon : _bYale University Press, _cc2021.
300			_ax, 572 p. : _bill. ; _c22 cm.
504			_aIncludes bibliographical references (pages 541-553) and index.
520			_aAn 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. - -
526			_aMathematics, Physics and Statistics
526			_aEconomics
590			_aSumaiya Kainat Bintey Kohinoor
650		0	_aCausation.
650		0	_aInference.
650		0	_aDependence (Statistics)
650		0	_aSocial sciences _xMethodology.
650		0	_aSocial sciences _xData processing.
942			_2lcc _cBK
999			_c30565 _d30565