meanbal: Means of the Covariates After Weighting

In ngreifer/sbw: Stable weights that balance covariates

Description

Function for tabulating the means of the covariates and assessing balance after weighting.

Usage

meanbal(data_frame_weights, t_ind, bal_covs, weights, target, digits = 2)

Arguments

data_frame_weights	a data frame with the treatment or nonresponse indicator, covariates, and weights organized in columns.
t_ind	a string with the name of the treatment or nonresponse indicator in data_frame_weights
bal_covs	a vector of strings with the names of the covariates in data_frame_weights for which it is desired to check balance.
weights	a string with the name of the weights in data_frame_weights.
target	a string that determines whether the weights were calculated to estimate: (i) the average treatment effect (in which case the target sample are all the units in the treatment or nonresponse indicator t_ind, and the 0's in t_ind are weighted to recover the structure of the observed covariates of all the units in t_ind; this is the option target="all"); (ii) the average treatment effect on the treated (which corresponds to defining the target sample as the elements with 1's in t_ind so the 0's in t_ind are weighted to "represent" the 1's; this is the option target="treated"); (iii) the average treatment effect on the controls (which does the opposite as option (ii) and weights the 1's in t_ind to recover the structure of the observed covariates of all the units with 0's in t_ind; this is the option target="controls").
digits	a scalar indicating the number of digits to display in the columns of the table.

Details

mean_bal is a function for tabulating the means of the covariates and assessing covariate balance after weighting.

Value

A table with the following columns:

Mean Target	mean of the target sample for each covariate;
Mean Weighted	mean of the weighted sample for each covariate;
Std. Difference	standardized differences in means after weighting for each covariate.

Author(s)

Jose R. Zubizarreta <[e-mail]>

References

Zubizarreta, J. R., "Stable Weights that Balance Covariates for Causal Inference and Estimation with Incomplete Data," submitted.

Examples

	# 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 = 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(1)
	n_obs = 200
	aux = kangschafer(n_obs)
	Z = aux$Z
	p = aux$p
	t = aux$t
	y = aux$y
	X = aux$X

	# Data frame
	t_ind = t
	bal_covs = X
	data_frame = as.data.frame(cbind(t_ind, bal_covs))
	names(data_frame) = c("t_ind", "X1", "X2", "X3", "X4")

	# Treatment indicator
	t_ind = "t_ind"

	# Moment covariates
	bal_covs = c("X1", "X2", "X3", "X4")

	# Moment tolerances
	bal_tols = 1/100

	# Whether the moment tolerances are expressed in standard deviations
	bal_tols_sd = TRUE

	# Here, the 0's in t_ind are weighted to "represent" the 1's and estimate the average treatment on the treated
	target = "all"

	# Here, the "ell-2" norm is used to minimize the variance of the weights
	l_norm = "l_2"

	# Minimum value of the weights
	w_min = 0

	# Here, the weights are constrained to add up to one
	normalize = 1

	# Solver
	solver = "quadprog"

	# Output display
	display = 0

	# Find optimal weights
	out = sbw(data_frame, t_ind, bal_covs, bal_tols, bal_tols_sd, target, l_norm, w_min, normalize, solver, display)

	# Check balance
	data_frame_weights = out$data_frame_weights
	t_ind = "t_ind"
	bal_covs = c("X1", "X2", "X3", "X4")
	weights = "weights"
	target = "all"
	meanbal(data_frame_weights, t_ind, bal_covs, weights, target, digits=2)

ngreifer/sbw documentation built on May 29, 2019, 3:17 p.m.