000			03559nam a2200433 i 4500
001			CR9781107587991
003			UkCbUP
005			20240905192430.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			131002s2015||||enk o ||1 0|eng|d
020			_a9781107587991 (ebook)
020			_z9781107065079 (hardback)
020			_z9781107694163 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aH62 _b.M646 2015
082	0	0	_a300.72 _223
100	1		_aMorgan, Stephen L. _q(Stephen Lawrence), _d1971- _eauthor.
245	1	0	_aCounterfactuals and causal inference : _bmethods and principles for social research / _cStephen L. Morgan, Christopher Winship.
246	3		_aCounterfactuals & Causal Inference
250			_aSecond edition.
264		1	_aCambridge : _bCambridge University Press, _c2015.
300			_a1 online resource (xxiii, 499 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aAnalytical methods for social research
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
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			_aIn 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.
650		0	_aSocial sciences _xMethodology.
650		0	_aCausation.
700	1		_aWinship, Christopher, _eauthor.
776	0	8	_iPrint version: _z9781107065079
830		0	_aAnalytical methods for social research.
856	4	0	_uhttps://doi.org/10.1017/CBO9781107587991
942			_2ddc _cEB
999			_c9352 _d9352