Package IUPS. February 19, 2015
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1 Package IUPS February 19, 2015 Type Package Title Incorporating Uncertainties in Propensity Scores Version 1.0 Date Author Weihua An, Huizi Xu, and Zhida Zheng, Indiana University Bloomington Maintainer Weihua An Depends R (>= 2.14), R2jags, Matching, boot SystemRequirements JAGS (>= 3.3.0) This package includes functions to incorporate uncertainties in estimated propensity scores and provide adjusted standard errors for making valid causal inference. License GPL-3 NeedsCompilation no Repository CRAN Date/Publication :56:42 R topics documented: bpsm bpsr modelpsm modelpsr sortps Index 8 1
2 2 bpsm bpsm Bayesian Propensity Score Matching A function includes both Bayesian and non-bayesian methods to incorporate uncertainties in estimated propensity scores and to provide adjusted standard errors for propensity score matching estimators. bpsm(y, t, X, estimand = "ATE", method = "AI", M = 1, L = 1, K = 10000, S = 1000) Arguments Y t X estimand method M L K S A vector containing the outcome variable. A vector containing the treatment indicator. A matrix containing the covariates. A character string for the estimand. The default is "ATE", average treatment effects. The other option is "ATT", average treatment effects on the treated. A character string for the method to adjust standard errors. The default is "AI", which uses the methods proposed by Abadie and Imbens (2011). "BPSM" invokes the Bayesian methods developed by An (2010). "Both" use both methods. Number of matches requested. Number of within-group matches requested for the AI method. Numbers of iterations. Only needed for BPSM. Number of posterior samples. Only needed for BPSM. Details Value Estimated propensity scores are used to match treated units with untreated units. The function includes two methods to take into account the uncertainties in the estimated propensity scores and to provide adjusted standard errors. The AI method is computationally more efficient but may produce negative standard errors in some cases. In contrast, the BPSM method always provides positive standard errors but requires longer computational time to converge, especially when the sample size is large. estimates estimand The estimated treatment effects and their adjusted standard errors. Phat shows the results with unadjusted standard errors. AI shows the results with adjusted standard errors based on the AI method. BP* shows the results with adjusted standard errors based on the Bayesian method. A character string indicting the estimand requested.
3 bpsm 3 time sims posterior The time elapsed for the computation. The number of iterations requested for the Bayesian computation, K The posterior sample distribution of the treatment effects. BPSM uses the posterior standard deviation to approximate the standard error. Note The latest verison of the Abadie and Imbens paper may be found at edu/fs/aabadie/pscore.pdf. To use BPSM, first install JAGS to the local computer. JAGS is available at Some parts of the code is still preliminary. So use at your own risk. References An, Weihua "Bayesian Propensity Score Estimators: Incorporating Uncertainties In Propensity Scores Into Causal Inference." Sociological Methodology 40: edu/~weihuaan/documents/2010_bpse.pdf. Abadie, Alberto and Guido W. Imbens Matching On the Estimated Propensity Score." Working Paper. bpsr, modelpsm, modelpsr, Match, sortps Examples library(boot) a = 2; b = c(1, -2, 5); N = 500 x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) X <- as.matrix(cbind(rep(1, N), x1, x2)) p <- inv.logit( X %*% b ) t <- rbinom(n, 1, p) e <- rnorm(n, 0, 1) Y <- 5 * t + a * ( x1 + x2 ) + e bpsm( Y, t, X, method = "AI", estimand = "ATE" )
4 4 bpsr bpsr Bayesian Propensity Score Regression A function uses Bayesian methods to incorporate uncertainties in estimated propensity scores and provide adjusted standard errors for propensity score regressions. bpsr(y, t, X, K = 10000, S = 1000) Arguments Y t X K S A vector containing the outcome variable. A vector containing the treatment indicator. A matrix containing the covariates. Numbers of iterations. Number of posterior samples. Details Value Estimated propensity scores are used as an additional covariate in the main outcome model to control for selection or to provide better control for the nonlinear effects of covariates. The function bpsr takes into account the uncertainties in estimating the propensity scores and adjusts the standard errors accordingly. estimates time sims posterior The estimated treatment effects and their adjusted standard errors. Phat shows the results with unadjusted standard errors. BPSR shows the results with adjusted standard errors using the Bayesian method. The time elapsed for the computation. The number of iterations requested for the Bayesian computation, K The posterior sample distribution of the treatment effects. The function bpsr uses the posterior standard deviation to approximate the standard error. Note To use bpsr, first install JAGS to the local computer. JAGS is available at sourceforge.net/.
5 modelpsm 5 References An, Weihua "Bayesian Propensity Score Estimators: Incorporating Uncertainties In Propensity Scores Into Causal Inference." Sociological Methodology 40: edu/~weihuaan/documents/2010_bpse.pdf. bpsm, modelpsm, modelpsr, Match, sortps Examples ## Not run: library(boot) a = 2; b = c(1, -2, 5); N = 500 x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) X <- cbind(rep(1, N), x1, x2) p <- inv.logit( X %*% b ) t <- rbinom(n, 1, p) e <- rnorm(n, 0, 1) Y <- 5 * t + a * ( x1 + x2 ) + e bpsr(y, t, X ) ## End(Not run) modelpsm modelpsm The JAGS model to feed in BPSM. It is an internal function called by BPSM. modelpsm() bpsm, bpsr, modelpsr, Match, sortps
6 6 sortps modelpsr modelpsr The JAGS model to feed in BPSR. It is an internal function called by BPSR. modelpsr() bpsm, bpsr, modelpsm, Match, sortps sortps Sorting Propensity Scores An internal function called by BPSM to sort propensity scores and match units with close propensity scores in the same treatment condition. sortps(d, N, N1, p2, L, Data) Arguments D N N1 p2 L Details Number of covariates including the intercept. Number of units. Number of treated units. Estimated propensity scores. Number of within-group matches requested for the AI method. Data A matrix containing Y, t, and X. The function generates the above arguments from the inputed data and returns IDs of the matched units with close propensity scores in the same treatment condition.
7 sortps 7 bpsm, bpsr, modelpsm, modelpsr, Match
8 Index bpsm, 2, 5 7 bpsr, 3, 4, 4, 5 7 Match, 3, 5 7 modelpsm, 3, 5, 5, 6, 7 modelpsr, 3, 5, 6, 7 sortps, 3, 5, 6, 6 8
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