By Walter A. Shewhart, Samuel S. Wilks(eds.)
Chapter 1 an summary of equipment for Causal Inference from Observational reports (pages 1–13): Sander Greenland
Chapter 2 Matching in Observational reports (pages 15–24): Paul R. Rosenbaum
Chapter three Estimating Causal results in Nonexperimental reports (pages 25–35): Rajeev Dehejia
Chapter four medicine expense Sharing and Drug Spending in Medicare (pages 37–47): Alyce S. Adams
Chapter five A comparability of Experimental and Observational info 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 businesses: Constraints, Inferential ambitions, and Robustness concerns (pages 109–115): John Eltinge
Chapter eleven Bridging throughout alterations in class structures (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 strategies 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 growth (pages 153–162): Neal Thomas
Chapter 15 Propensity ranking Estimation with lacking information (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 mix types and issue versions (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 strong replacement to Logistic and Probit Regression (pages 227–238): Chuanhai Liu
Chapter 22 utilizing EM and information Augmentation for the Competing hazards version (pages 239–251): Radu V. Craiu and Thierry Duchesne
Chapter 23 combined results types 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 traces in High?Energy Astrophysics (pages 285–296): David A. van Dyk and Taeyoung Park
Chapter 27 stronger 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 deciding upon most probably Duplicates via list 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 using 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
Read or Download Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family PDF
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Additional info for Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family
In columns (5) and (8), controlling for covariates has little impact on the stratification and matching estimates. Likewise for the CPS, the propensity-score-based estimates from the CPS—$1,713 and $1,582—are much closer to the experimental benchmark than estimates from the full comparison sample: −$8,498 and $972. 2 illustrates the value of allowing both for a heterogeneous treatment effect and for a nonlinear functional form in the propensity score. The estimators in columns (4) to (8) have both these characteristics, whereas in where δ is the treatment effect and we include age, age2 , education, no degree, black, Hispanic, RE74, and RE75 as controls.
Because we obtain this subset by looking at pretreatment covariates, we do not CAUSAL EFFECTS IN NONEXPERIMENTAL STUDIES—DEHEJIA 31 the covariates for the treatment and control groups is not significantly different. 1 presents the sample characteristics of the two comparison groups and the treatment group. The differences are striking: the PSID and CPS sample units are eight to nine years older than those in the NSW group; their ethnic and racial composition is different; they have on average completed high school degrees, while NSW participants were by and large high school dropouts; and, most dramatically, pretreatment earnings are much higher, by more than 10, 000, for the comparison units than for the treated units, by more than $10,000.
The (nonexperimental) comparison group is drawn from a different population (in our application both the Current Population Survey [CPS] and Panel Survey of Income Dynamics [PSID] are more representative of the general US population). Thus, the treatment effect we are trying to identify is the average treatment effect for the treated population: τ |T =1 = E(Y1i |Ti = 1) − E(Y0i |Ti = 1). 4) where the outer expectation is over the distribution of p(Xi )|Ti = 1, namely the distribution of the propensity score in the treated group.