Multiple Imputation: Joint and Conditional Modeling of Missing DataKazuki Yoshida
This is a student-created document on multiple imputation focusing on the two major approaches of modeling missing data: the joint and conditional approaches.
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Kazuki Yoshida
Presentation at the Epidemiology Congress of Americas 2016.
https://epiresearch.org/2016-meeting/submitted-abstract-sessions/pharmacoepidemiology-estimation-of-treatment/
Paper: http://journals.lww.com/epidem/Abstract/publishahead/Matching_weights_to_simultaneously_compare_three.98901.aspx (email me at kazukiyoshida@mail.harvard.edu)
Simulation code: https://github.com/kaz-yos/mw
Tutorial: http://rpubs.com/kaz_yos/matching-weights
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
52. マッチングによる因果効果の推定
## パッケージの読み込み
library(Matching)
nsw1 <- Match(Y = nsw.data$re78, Tr = nsw.data$treat, X = logit$fitted)
summary(nsw1)
……(略)……
Estimate... 1866.4
AI SE...... 1562.3
T-stat..... 1.1947
p.val...... 0.23222
Original number of observations.......................... 313
Original number of treated obs............................ 185
Matched number of observations........................ 185
Matched number of observations (unweighted). 202
NSWプログラムの効果
62. ・Austin PC (2011) An Introduction to Propensity
Score Methods for Reducing the Effects of
Confounding in Observational Studies.
・Stuart EA (2010) Matching Methods for Causal
Inference: A Review and a Look Forward.
・Sekhon JA (2011) Multivariate and Propensity Score
Matching Software with Automated Balance
Optimization: The Matching Package for R.
参考資料(論文)