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_a020079935 _2Uk |
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_aQ175.32.C38 _bC86 2021 |
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_a300.72 C917C _223 |
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_a501 _223 |
| 100 | 1 |
_aCunningham, Scott. _930737 |
|
| 245 | 1 | 0 |
_aCausal inference : _bthe mixtape _cScott Cunningham |
| 260 |
_aNew Haven: _bYale University Press, _c2021. |
||
| 300 |
_ax, 572 pages : _billustrations ; _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. - - | ||
| 650 | 0 |
_aCausation. _930738 |
|
| 650 | 0 |
_aInference. _930739 |
|
| 650 | 0 |
_aDependence (Statistics) _930740 |
|
| 650 | 0 |
_aSocial sciences _xMethodology. _930741 |
|
| 650 | 0 |
_aSocial sciences _xData processing. _930742 |
|
| 650 | 7 |
_aBUSINESS & ECONOMICS / Econometrics. _2bisacsh _930743 |
|
| 650 | 7 |
_aCausation. _2fast _930738 |
|
| 650 | 7 |
_aInference. _2fast _930739 |
|
| 650 | 7 |
_aDependence (Statistics) _2fast _930740 |
|
| 650 | 7 |
_aSocial sciences _xData processing. _2fast _930742 |
|
| 650 | 7 |
_aSocial sciences _xMethodology. _2fast _930741 |
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| 906 |
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