000			03663nam a22004337a 4500
001			18289483
003			OSt
005			20180928172638.0
008			180928b xxu||||| |||| 00| 0 eng d
010			_a 2014033205
020			_a9781107065079 (hardback)
020			_a9781107694163 (paperback) : _c£29.99
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
082	0	0	_a300.72 _223
084			_aMAT029000 _2bisacsh
100	1		_aMorgan, Stephen L. _q(Stephen Lawrence), _d1971-
245	1	0	_aCounterfactuals and causal inference : _bmethods and principles for social research / _cStephen L. Morgan, Christopher Winship.
250			_aSecond Edition
264		1	_aNew York, NY : _bCambridge University Press, _c2015.
300			_axxiii, 499 pages : _billustrations ; _c26 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
365			_2GBP _a£ _b£29.99 _c₹ _d1 GBP = ₹ 94.00
490	0		_aAnalytical methods for social research
500			_aRevised edition of the authors' Counterfactuals and causal inference, published in 2007.
504			_aIncludes bibliographical references (pages 451-496) and index.
505	8		_aMachine generated contents note: Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction; Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model; 3. Causal graphs; Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators; 5. Matching estimators of causal effects; 6. Regression estimators of causal effects; 7. Weighted regression estimators of causal effects; Part IV. Estimating Causal Effects When Backdoor Conditioning is Ineffective: 8. Self-selection, heterogeneity, and causal graphs; 9. Instrumental-variable estimators of causal effects; 10. Mechanisms and causal explanation; 11. Repeated observations and the estimation of causal effects; Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis; Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.
520			_a"In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed"-- _cProvided by publisher.
650		0	_aSocial sciences _xResearch.
650		0	_aSocial sciences _xMethodology.
650		0	_aCausation.
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh
700	1		_aWinship, Christopher.
856	4	2	_3Cover image _uhttp://assets.cambridge.org/97811070/65079/cover/9781107065079.jpg
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cM
999			_c12323 _d12323