Description Usage Arguments Value Source References Examples
Function for finding stable weights (that is, weights of minimum variance) that approximately balance the empirical distribution of the observed covariates.
1 2 3 4 5 6 7 8 9 10  sbw(
dat,
ind = NULL,
out = NULL,
bal = list(bal_cov, bal_alg = TRUE, bal_tol, bal_std = "group", bal_gri = c(1e04,
0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1), bal_sam = 1000),
wei = list(wei_sum = TRUE, wei_pos = TRUE),
sol = list(sol_nam = "quadprog", sol_dis = FALSE),
par = list(par_est = "att", par_tar = NULL)
)

dat 
data, a data frame with a treatment assignment or missingness indicator, covariates, and possibly outcomes (which are optional). 
ind 
treatment assignment or missingness indicator, a string with the name of the binary treatment or missingness indicator, equal to 1 if treated (missing) and 0 otherwise.
When 
out 
outcome, a vector of strings with the names of the outcome variables. The default is 
bal 
balance requirements, a list with the requirements for covariate balance with the form

wei 
weighting constraints, a list with all the weighting constraints with the form

sol 
solver, a list that specifies the solver option with the form
See the POGS manual for details. 
par 
parameter of interest, a list describing the parameter of interest or estimand with the form

A list with the following elements:
dat_weights
, a data frame with the optimal weights dat_weights$sbw_weights
;
ind
, an argument provided by the user;
out
, an argument provided by the user;
bal
, an argument provided by the user;
wei
, an argument provided by the user;
sol
, an argument provided by the user;
par
, an argument provided by the user;
effective_sample_size
, effective sample size/sizes for the weighted group/groups;
objective_value
, value/values of the objective function/functions at the optimum;
status
, status of the solution. If the optimal weights are found, status = optimal
;
otherwise, the solution may be not optimal or not exist, in which case an error will be returned with details specific to the solver used.
For the solver "quadprog", the status code is missing, therefore, status = NA
;
time
, time elapsed to find the optimal solution;
shadow_price
, dual variables or shadow prices of the covariate balance constraints;
balance_parameters
, details of the balance parameters;
cstat
, covariate balance statistic used in Wang and Zubizarreta (2020).
A magnitude to be minimized to select the degree of approximate balance in bal$bal_gri
.
https://www.ibm.com/products/ilogcplexoptimizationstudio
https://www.gurobi.com/products/gurobioptimizer/
https://www.mosek.com/products/mosek/
http://foges.github.io/pogs/stp/r
Chattopadhyay, A., Hase, C. H., and Zubizarreta, J. R. (2020), "Balancing Versus Modeling Approaches to Weighting in Practice," Statistics in Medicine, in press.
Kang, J. D. Y., and Schafer, J. L. (2007), "Demistifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data," Statistical Science, 22, 523539.
Stuart, E. A. Matching methods for causal inference: a review and a look forward. Statistical Science 2010; 25(1): 121.
Wang, Y., and Zubizarreta, J. R. (2020), "Minimal Dispersion Approximately Balancing Weights: Asymtotic Properties and Practical Considerations," Biometrika, 107, 93105.
Zubizarreta, J. R. (2015), "Stable Weights that Balance Covariates for Estimation with Incomplete Outcome Data," Journal of the American Statistical Association, 110, 910922.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88  # Simulate data
kangschafer = function(n_obs) {
# Z are the true covariates
# t is the indicator for the respondents (treated)
# y is the outcome
# X are the observed covariates
# Returns Z, t y and X sorted in decreasing order by t
Z = MASS::mvrnorm(n_obs, mu=rep(0, 4), Sigma=diag(4))
p = 1/(1+exp(Z[, 1].5*Z[, 2]+.25*Z[, 3]+.1*Z[, 4]))
t = rbinom(n_obs, 1, p)
Zt = cbind(Z, p, t)
Zt = Zt[order(t), ]
Z = Zt[, 1:4]
p = Zt[, 5]
t = Zt[, 6]
y = 210+27.4*Z[, 1]+13.7*Z[, 2]+13.7*Z[, 3]+13.7*Z[, 4]+rnorm(n_obs)
X = cbind(exp(Z[, 1]/2), (Z[, 2]/(1+exp(Z[, 1])))+10, (Z[, 1]*Z[, 3]/
25+.6)^3, (Z[, 2]+Z[, 4]+20)^2)
return(list(Z=Z, p=p, t=t, y=y, X=X))
}
set.seed(1234)
n_obs = 200
aux = kangschafer(n_obs)
Z = aux$Z
p = aux$p
t = aux$t
y = aux$y
X = aux$X
# Generate data frame
t_ind = t
bal_cov = X
data_frame = as.data.frame(cbind(t_ind, bal_cov, y))
names(data_frame) = c("t_ind", "X1", "X2", "X3", "X4", "Y")
# Define treatment indicator and
t_ind = "t_ind"
# moment covariates
bal = list()
bal$bal_cov = c("X1", "X2", "X3", "X4")
# Set tolerances
bal$bal_tol = 0.02
bal$bal_std = "group"
# Solve for the Average Treatment Effect on the Treated, ATT (default)
bal$bal_alg = FALSE
sbwatt_object = sbw(dat = data_frame, ind = t_ind, out = "Y", bal = bal)
# # Solve for a Conditional Average Treatment Effect, CATE
# sbwcate_object = sbw(dat = data_frame, ind = t_ind, out = "Y", bal = bal,
# sol = list(sol_nam = "quadprog"), par = list(par_est = "cate", par_tar = "X1 > 1 & X3 <= 0.22"))
# # Solve for the population mean, POP
# tar = colMeans(bal_cov)
# names(tar) = bal$bal_cov
# sbwpop_object = sbw(dat = data_frame, ind = t_ind, out = "Y", bal = bal,
# sol = list(sol_nam = "quadprog"), par = list(par_est = "pop"))
# # Solve for a target population mean, AUX
# sbwaux_object = sbw(dat = data_frame, bal = bal,
# sol = list(sol_nam = "quadprog"), par = list(par_est = "aux", par_tar = tar*1.05))
# # Solve for the ATT using the tuning algorithm
# bal$bal_alg = TRUE
# bal$bal_sam = 1000
# sbwatttun_object = sbw(dat = data_frame, ind = t_ind, out = "Y", bal = bal,
# sol = list(sol_nam = "quadprog"), par = list(par_est = "att", par_tar = NULL))
# Check
summarize(sbwatt_object)
# summarize(sbwcate_object)
# summarize(sbwpop_object)
# summarize(sbwaux_object)
# summarize(sbwatttun_object)
# Estimate
estimate(sbwatt_object)
# estimate(sbwcate_object)
# estimate(sbwpop_object)
# estimate(sbwatttun_object)
# Visualize
visualize(sbwatt_object)
# visualize(sbwcate_object)
# visualize(sbwpop_object)
# visualize(sbwaux_object)
# visualize(sbwatttun_object)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.