LEADER 02844oam 2200733I 450 001 9910456037003321 005 20200520144314.0 010 $a1-134-90703-6 010 $a1-280-32109-1 010 $a0-203-41657-0 010 $a0-203-31181-7 024 7 $a10.4324/9780203416570 035 $a(CKB)111087027069736 035 $a(EBL)168032 035 $a(OCoLC)560307962 035 $a(SSID)ssj0000143041 035 $a(PQKBManifestationID)11158941 035 $a(PQKBTitleCode)TC0000143041 035 $a(PQKBWorkID)10110045 035 $a(PQKB)10802706 035 $a(MiAaPQ)EBC168032 035 $a(Au-PeEL)EBL168032 035 $a(CaPaEBR)ebr10099190 035 $a(CaONFJC)MIL32109 035 $a(OCoLC)52479981 035 $a(EXLCZ)99111087027069736 100 $a20180331d1993 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEcology, policy, and politics $ehuman well-being and the natural world /$fJohn O'Neill 210 1$aLondon ;$aNew York :$cRoutledge,$d1993. 215 $a1 online resource (240 p.) 225 1 $aEnvironmental Philosophies 300 $aDescription based upon print version of record. 311 $a1-138-42451-X 311 $a0-415-07300-6 320 $aIncludes bibliographical references (p. 209-219) and index. 327 $aBook Cover; Title; Contents; Acknowledgements; HUMAN WELL-BEING AND THE NATURAL WORLD; NATURE, INTRINSIC VALUE AND HUMAN WELL-BEING; FUTURE GENERATIONS AND THE HARMS WE DO OURSELVES; THE CONSTITUENCY OF ENVIRONMENTAL POLICY; JUSTIFYING COST-BENEFIT ANALYSIS: ARGUMENTS FROM WELFARE; PLURALISM, LIBERALISM AND THE GOOD LIFE; PLURALISM, INCOMMENSURABILITY, JUDGEMENT; AUTHORITY, DEMOCRACY AND THE ENVIRONMENT; SCIENCE, POLICY AND ENVIRONMENTAL VALUE; MARKET, HOUSEHOLD AND POLITICS; Notes; Bibliography; Index 330 $aRevealing flaws in both 'green' and market-based approaches to environmental policy, O'Neill develops an Aristotolian account of well-being. He examines the implications for wider issues involving markets, civil society and politics. 410 0$aEnvironmental Philosophies 606 $aAnimal welfare 606 $aConservation of natural resources 606 $aEcology 606 $aEnvironmental policy 606 $aHuman ecology 608 $aElectronic books. 615 0$aAnimal welfare. 615 0$aConservation of natural resources. 615 0$aEcology. 615 0$aEnvironmental policy. 615 0$aHuman ecology. 676 $a333.7 676 $a363.7 700 $aO'Neill$b John$f1956,$0953533 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910456037003321 996 $aEcology, policy, and politics$92156059 997 $aUNINA LEADER 05882nam 22007454a 450 001 9911018949303321 005 20200520144314.0 010 $a9786610268986 010 $a9781280268984 010 $a1280268980 010 $a9780470090459 010 $a0470090456 010 $a9780470090442 010 $a0470090448 035 $a(CKB)1000000000377283 035 $a(EBL)219751 035 $a(OCoLC)56717813 035 $a(SSID)ssj0000104656 035 $a(PQKBManifestationID)11130714 035 $a(PQKBTitleCode)TC0000104656 035 $a(PQKBWorkID)10100494 035 $a(PQKB)10143714 035 $a(MiAaPQ)EBC219751 035 $a(iGPub)WILEYB0023891 035 $a(PPN)19687159X 035 $a(Perlego)2752060 035 $a(EXLCZ)991000000000377283 100 $a20040720d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplied Bayesian modeling and causal inference from incomplete-data perspectives $ean essential journey with Donald Rubin's statistical family /$fedited by Andrew Gelman, Xiao-Li Meng 205 $a1st ed. 210 $aChichester, West Sussex, England ;$aHoboken, NJ $cWiley$dc2004 215 $a1 online resource (437 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780470090435 311 08$a047009043X 320 $aIncludes bibliographical references (p. 361-400) and index. 327 $aApplied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction 327 $a3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 327 $a7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 327 $a9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 327 $a11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 327 $a14.2 Statistical methods in NAEP 330 $aThis book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include:Comprehensive coverage of an imporant area for both research and applications.Adopts a pragmatic approach to describ 410 0$aWiley series in probability and statistics. 606 $aBayesian statistical decision theory 606 $aMissing observations (Statistics) 615 0$aBayesian statistical decision theory. 615 0$aMissing observations (Statistics) 676 $a519.5/42 701 $aRubin$b Donald B$0102932 701 $aGelman$b Andrew$044041 701 $aMeng$b Xiao-Li$0970587 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911018949303321 996 $aApplied Bayesian modeling and causal inference from incomplete-data perspectives$94422125 997 $aUNINA