000			03548nam a2200409 i 4500
001			017042426
003			OSt
005			20230309151439.0
008			140904s2015 nyua b 001 0 eng
010			_a 2014033205
015			_aGBB682937 _2bnb
016	7		_a017042426 _2Uk
020			_a9781107694163 (paperback)
040			_aDLC _beng _cIISERB _dDLC _dUk _erda
042			_aukblcatcopy
050	0	0	_aH62 _b.M646 2015
082	0	0	_a300.72 M821C2 _223
084			_aMAT029000 _2bisacsh
100	1		_aMorgan, Stephen L. _929045
245	1	0	_aCounterfactuals and causal inference : _bmethods and principles for social research _cStephen L. Morgan, Christopher Winship.
250			_aSecond Edition.
260			_aCambridge: _bCambridge University Press, _c2020.
300			_axxiii, 499 pages : _billustrations ; _c26 cm.
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"--
650		0	_aSocial sciences _xResearch. _929046
650		0	_aSocial sciences _xMethodology. _929047
650		0	_aCausation. _929048
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh _929049
700	1		_aWinship, Christopher. _929050
856	4	2	_3Cover image _uhttp://assets.cambridge.org/97811070/65079/cover/9781107065079.jpg
942			_2ddc _cBK
999			_c9977 _d9977