Applied Bayesian Modeling and Causal Inference from by Walter A. Shewhart, Samuel S. Wilks(eds.)
By Walter A. Shewhart, Samuel S. Wilks(eds.)
Chapter 1 an outline of tools for Causal Inference from Observational experiences (pages 1–13): Sander Greenland
Chapter 2 Matching in Observational experiences (pages 15–24): Paul R. Rosenbaum
Chapter three Estimating Causal results in Nonexperimental reports (pages 25–35): Rajeev Dehejia
Chapter four medicine price Sharing and Drug Spending in Medicare (pages 37–47): Alyce S. Adams
Chapter five A comparability of Experimental and Observational information Analyses (pages 49–60): Jennifer L. Hill, Jerome P. Reiter and Elaine L. Zanutto
Chapter 6 solving damaged Experiments utilizing the Propensity ranking (pages 61–71): Bruce Sacerdote
Chapter 7 The Propensity rating with non-stop remedies (pages 73–84): Keisuke Hirano and Guido W. Imbens
Chapter eight Causal Inference with Instrumental Variables (pages 85–96): Junni L. Zhang
Chapter nine critical Stratification (pages 97–108): Constantine E. Frangakis
Chapter 10 Nonresponse Adjustment in govt Statistical organisations: Constraints, Inferential pursuits, and Robustness matters (pages 109–115): John Eltinge
Chapter eleven Bridging throughout adjustments in type platforms (pages 117–128): Nathaniel Schenker
Chapter 12 Representing the Census Undercount via a number of Imputation of families (pages 129–140): Alan M. Zaslavsky
Chapter thirteen Statistical Disclosure suggestions in keeping with a number of Imputation (pages 141–152): Roderick J. A. Little, Fang Liu and Trivellore E. Raghunathan
Chapter 14 Designs generating Balanced lacking information: Examples from the nationwide review of academic development (pages 153–162): Neal Thomas
Chapter 15 Propensity ranking Estimation with lacking facts (pages 163–174): Ralph B. D'Agostino
Chapter sixteen Sensitivity to Nonignorability in Frequentist Inference (pages 175–186): Guoguang Ma and Daniel F. Heitjan
Chapter 17 Statistical Modeling and Computation (pages 187–194): D. Michael Titterington
Chapter 18 therapy results in Before?After info (pages 195–202): Andrew Gelman
Chapter 19 Multimodality in combination versions and issue types (pages 203–213): Eric Loken
Chapter 20 Modeling the Covariance and Correlation Matrix of Repeated Measures (pages 215–226): W. John Boscardin and Xiao Zhang
Chapter 21 Robit Regression: an easy powerful substitute to Logistic and Probit Regression (pages 227–238): Chuanhai Liu
Chapter 22 utilizing EM and knowledge Augmentation for the Competing hazards version (pages 239–251): Radu V. Craiu and Thierry Duchesne
Chapter 23 combined results versions and the EM set of rules (pages 253–264): Florin Vaida, Xiao?Li Meng and Ronghui Xu
Chapter 24 The Sampling/Importance Resampling set of rules (pages 265–276): Kim?Hung Li
Chapter 25 Whither utilized Bayesian Inference? (pages 277–284): Bradley P. Carlin
Chapter 26 effective EM?type Algorithms for becoming Spectral strains in High?Energy Astrophysics (pages 285–296): David A. van Dyk and Taeyoung Park
Chapter 27 enhanced Predictions of Lynx Trappings utilizing a organic version (pages 297–308): Cavan Reilly and Angelique Zeringue
Chapter 28 list Linkage utilizing Finite combination types (pages 309–318): Michael D. Larsen
Chapter 29 selecting most probably Duplicates via checklist Linkage in a Survey of Prostitutes (pages 319–329): Thomas R. Belin, Hemant Ishwaran, Naihua Duan, Sandra H. Berry and David E. Kanouse
Chapter 30 utilizing Structural Equation types with Incomplete facts (pages 331–342): Hal S. Stern and Yoonsook Jeon
Chapter 31 Perceptual Scaling (pages 343–360): Ying Nian Wu, Cheng?En Guo and tune Chun Zhu
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Extra resources for Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family
J , and Pr (Z |x ) follows McCullagh’s (1980) ordinal logit model, log Pr (Z ≥ j |x ) = αj + xT β, j = 0, 1, . . , J, 1 − Pr (Z ≥ j |x ) then xT β has the key properties of the propensity score for the two treatment levels described above. See Lu, Zanutto, Hornik, and Rosenbaum (2001) for an application of this approach, and see Imbens (2000) for an alternative approach with separate propensity scores for each level of Z. For many treatments, the decision is to treat now or to wait and see, possibly treating later.
Disturb the balance in observed and unobserved characteristics between the experimental treated and control groups. See Dehejia and Wahba (1999) for a comparison of the two samples. 4 These are the CPS-1 and PSID-1 comparison groups from Lalonde’s paper. 5 We use the following specifications for the propensity score. For the PSID, Prob(T = 1) = i F(age, age2 , education, education2 , married, no degree, black, Hispanic, RE74, RE75, RE742 , RE752 , u74 × black). For the CPS, Prob(Ti = 1) = F(age, age2 , education, education2 , no degree, married, black, Hispanic, RE74, RE75, u74, u75, educ × RE74, age3 ).
Matching with doses, as in Lu, Zanutto, Hornik, and Rosenbaum (2001), requires “nonbipartite matching” for which Derigs (1988) presents an algorithm and Fortran code. An alternative general algorithm is available in C; see Galil (1986). Covariance adjustment of matched data Rubin (1973b, 1979) found using simulations that covariance adjustment of matched pairs was more efficient than matching alone and more robust to model misspecification than covariance adjustment alone. In particular, covariance adjustment of matched pair differences consistently reduced bias, even when the covariance adjustment model was wrong, but covariance adjustment alone sometimes increased the bias compared to no adjustment when the model was wrong.