000 | 02028cam a22003017i 4500 | ||
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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 |
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041 | _aenm | ||
082 | 0 | 4 |
_a300.72 _bC917c _223a ed. |
100 |
_aCunningham, Scott _973010 |
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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 |
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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 |
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999 |
_c258902 _d258902 |