000			02028cam a22003017i 4500
001			21537797
005			20231124114729.0
007			ta
008			200520s2021 us a grb 001 0 eng d
020			_a978-0-300-25168-5
040			_aYDX _beng _cEC-QuPUC _erda
041			_aenm
082	0	4	_a300.72 _bC917c _223a ed.
100			_aCunningham, Scott _973010
245	1	0	_aCausal inference : _bthe mixtape / _cScott Cunningham
264		1	_aNew Haven ; _aLondres : _bYale University Press, _c2021
300			_aix, 572 pages : _billustrations ; _c22 cm
336			_atxt
337			_an
338			_anc
504			_aIncluye referencias bibliográficas (páginas 541-553) e índice
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. - -
650		7	_aCiencias sociales _xInvestigación _949942 _2ARMARC 2.0 en línea
650		7	_aCiencias sociales _xEstadística _949940 _2BGPUCE
650		7	_aCiencias sociales _xProceso de datos _2 _949931
856			_uhttps://puce.odilo.us/info/causal-inference-the-mixtape-03132491
942			_cBK _00
999			_c258902 _d258902