dbw: Doubly robust distribution balancing weighting (DBW)...
In dbw: Doubly Robust Distribution Balancing Weighting Estimation
dbw R Documentation
Doubly robust distribution balancing weighting (DBW) estimation
Description
dbw estimates a pre-specified parameter of interest (e.g.,
the average treatment effects (ATE) or the average treatment effects
on the treated (ATT)) with the augmented inverse probability weighting
(AIPW), where propensity scores are estimated using estimating
equations suitable for the parameter of interest and outcome models are
estimated using inverse probability weights. dbw can
also be used to estimate average outcomes (AO) in missing outcome cases.
Usage
dbw(
formula_y,
formula_ps,
estimand = "ATE",
method = "dbw",
method_y = "wls",
data,
normalize = TRUE,
vcov = TRUE,
lambda = 0,
weights = NULL,
clevel = 0.95,
tol = 1e-10,
init_lambda = 0.01
)
Arguments
formula_y
an object of class formula (or one that
can be coerced to that class): a symbolic description of the potential
outcome model to be fitted. This is a model for potential outcomes, so do not
include treatment variable in the model. When you want to use non-DR type
estimators, only include "1" in the right hand side of the formula for the
Hajek estimator and only include "0" for the Horvitz-Thompson estimator.
See example below for more details.
formula_ps
an object of class formula (or one that
can be coerced to that class): a symbolic description of the propensity score
model to be fitted. For the entropy balancing weighting (method = "eb"),
variables in the right hand side of the formula will be mean balanced.
estimand
a character string specifying a parameter of interest. Choose
"ATT" for the average treatment effects on the treated estimation, "ATE"
for the average treatment effects estimation, "ATC" for the average
outcomes estimation in missing outcome cases. You can choose "ATEcombined"
for the combined estimation for the average treatment effects estimation
when using the covariate balancing weighting (method = "cb").
method
a character string specifying a method for propensity score
estimation. Choose "dbw" for the distribution balancing weighting, "cb"
for the covariate balancing weighting, "eb" for the entropy balancing
weighting, and "mle" for the logistic regression with the maximum
likelihood estimation.
method_y
a character string specifying a method for potential outcome
prediction. Choose "wls" for the linear model, "logit" for the logistic
regression, "gam" for the generalized additive model for the continuous
outcome, and "gambinom" for the generalized additive model for the binary
outcome. Note that variance-covariance matrix is calculated only when
method_y = "wls" or method_y = "logit".
data
a data frame (or one that can be coerced to that class)
containing the outcomes and the variables in the model.
normalize
a logical parameter indicating whether to normalize the estimated
weights to sum up to one for each treatment group for method = "dbw". Default is TRUE.
vcov
a logical parameter indicating whether to estimate the variance.
Default is TRUE.
lambda
a parameter taking 0 or larger specifying the degree of the
L2-regularization for propensity score estimation. lambda = 0 (default)
means no regularization.
weights
an optional vector of ‘prior weights’ (e.g. sampling weights)
to be used in the fitting process. Should be NULL or a numeric vector.
clevel
confidence levels. Default is 0.95.
tol
a tolerance parameter for method = "dbw". Default is 1e-10.
init_lambda
a parameter for method = "dbw" to set the lambda value
for the initial values estimation, where lambda_init = lambda * init_lambda.
Default is 0.01.
Details
The treatment variable (or, response variable in missing outcome cases)
must be binary and coded as 0 (for controlled or missing observations)
or 1 (for treated or non-missing observations).
When the data frame has incomplete cases, which have NAs for either of
the treatment variable, the outcome variable, or explanatory variables
either for propensity score or outcome model estimation, dbw conducts
listwise deletion. Returned values (e.g., est_weights, ps, data)
do not contain values for these deleted cases.
For propensity score estimation, dbw can utilize the distribution
balancing weighting (method = "dbw"), covariate balancing weighting
(method = "cb"), entropy balancing weighting (method = "eb"), or
standard maximum likelihood estimation (method = "mle"). For the
covariate balancing weighting and entropy balancing weighting, dbw runs
much faster than the original functions (CBPS and ebalance)
by using loss-function-based algorithms, which also results in more
accurate covariate balance. For the ATT and ATC estimation, the distribution
balancing weighting, covariate balancing weighting, and entropy balancing
weighting are theoretically equivalent and dbw implements accordingly.
The parameter of interest is estimated by the AIPW estimator, where inverse
probability weights are standardized within each treatment group by being
devided by the size of the group after being calculated as
t_i / \pi_i - (1 - t_i) / (1 - \pi_i) for the ATE estimation,
(t_i - \pi_i) / (1 - \pi_i) for the ATT estimation,
(t_i - \pi_i) / \pi_i for the ATC estimation, and
t_i / \pi_i for the missing outcome cases. The resulting inverse probability
weights sum to 1 for the distribution balancing weighting, covariate
balancing weighting, and entropy balancing weighting estimators without
regularization.
The variance-covariance matrix for the parameter of interest and ancillary
parameters is calculated using the sandwich variance formula obtained in the
M-estimation framework.
When using regularization for propensity score estimation (lambda > 0),
you should standardize the covariates for propensity score estimation
by std_comp before using dbw. See example below for more details.
For the ATE estimation, it is recommended to specify the estimand as
"ATE", but you may specify it as "ATEcombined" when using the
covariate balancing weighting. The former utilizes the separated propensity
score estimation whereas the latter utilizes the combined estimation, and
the former should produce smaller biases and variances. Note that the
former estimates two propensity scores for each observation by estimating
two propensity score functions with different estimating equations.
For the AO estimation, NA values for the outcome variable for missing cases
(the response variable taking "0") are not deleted. For this processing, the
outcome variable name must not contain spaces.
Value
dbw returns an object of "dbw" class.
The function summary (i.e., summary.dbw) can be used to obtain or print a
summary of the results.
An object of class "dbw" is a list containing the following components:
est
the point estimate of the parameter of interest.
coef_ps
a named vector of coefficients for propensity score estimation.
A list of two sets of coefficients for two sets of propensity scores
(one for estimating E[Y(1)] and the other for estimating E[Y(0)]) is
returned when estimand = "ATE".
coef_y
a named vector of coefficients for outcome model estimation.
A list of two sets of coefficients for two sets of outcome models
(one for estimating E[Y(1)] and the other for estimating E[Y(0)]) is
returned when estimand = "ATE" and estimand = "ATEcombined".
varcov
the variance-covariance matrix of the coefficients and the
parameter of interest.
est_weights
the estimated inverse probability weights.
ps
the estimated propensity scores. A list of two sets of the
estimated propensity scores (one for estimating E[Y(1)] and the other
for estimating E[Y(0)]) is returned when estimand = "ATE".
predicted_y
the predicted outcomes. A list of two sets of the
predicted outcomes (one for estimating E[Y(1)] and the other for
estimating E[Y(0)]) is returned when estimand = "ATE" and
estimand = "ATEcombined".
converged
logical. Were the propensity score estimation algorithms
judged to have converged?
effn
the effective sample size for the parameter of interest estimation.
effn_original
the effective sample size with the initial weights.
estimand
the parameter of interest specified.
method
the method for propensity score estimation specified.
method_y
the method for outcome model estimation specified.
response
the treatment vector. The response (non-missingness) vector
when the missing outcome cases.
outcome
the outcome vector.
original_weights
the weights initially supplied, a vector of 1s if
none were.
ci
a matrix of the confidence intervals for the parameter of interest.
formula_y
the outcome model formula specified.
formula_ps
the propensity score model formula specified.
call
the matched call.
data
the data argument.
normalize
a logical argument indicating whether to normalize the
estimated weights for each treatment group for method = "dbw".
lambda
the parameter specifying the degree of the L2-regularization.
Author(s)
Hiroto Katsumata
References
Katsumata, Hiroto. 2024. "How Should We Estimate Inverse
Probability Weights with Possibly Misspecified Propensity Score Models?"
Political Science Research and Methods.
Imai, Kosuke and Marc Ratkovic. 2014. "Covariate Balancing Propensity Score."
Journal of the Royal Statistical Society, Series B (Statistical Methodology)
76 (1): 243–63.
Hainmueller, Jens. 2012. "Entropy Balancing for Causal Effects: A
Multivariate Reweighting Method to Produce Balanced Samples in
Observational Studies." Political Analysis 20 (1): 25–46.
See Also
summary.dbw, std_comp, gam
Examples
# Simulation from Kang and Shafer (2007) and Imai and Ratkovic (2014)
# ATE estimation
# True ATE is 10
tau <- 10
set.seed(12345)
n <- 1000
X <- matrix(stats::rnorm(n * 4, mean = 0, sd = 1), nrow = n, ncol = 4)
prop <- 1 / (1 + exp(X[, 1] - 0.5 * X[, 2] +
0.25 * X[, 3] + 0.1 * X[, 4]))
treat <- rbinom(n, 1, prop)
y <- 210 +
27.4 * X[, 1] + 13.7 * X[, 2] + 13.7 * X[, 3] + 13.7 * X[, 4] +
tau * treat + stats::rnorm(n = n, mean = 0, sd = 1)
ybinom <- (y > 210) + 0
df0 <- data.frame(X, treat, y, ybinom)
colnames(df0) <- c("x1", "x2", "x3", "x4", "treat", "y", "ybinom")
# Variables for a misspecified model
Xmis <- data.frame(x1mis = exp(X[, 1] / 2),
x2mis = X[, 2] * (1 + exp(X[, 1]))^(-1) + 10,
x3mis = (X[, 1] * X[, 3] / 25 + 0.6)^3,
x4mis = (X[, 2] + X[, 4] + 20)^2)
# Data frame and formulas for propensity score estimation
df <- data.frame(df0, Xmis)
formula_ps_c <- stats::as.formula(treat ~ x1 + x2 + x3 + x4)
formula_ps_m <- stats::as.formula(treat ~ x1mis + x2mis +
x3mis + x4mis)
# Formula for a misspecified outcome model
formula_y <- stats::as.formula(y ~ x1mis + x2mis + x3mis + x4mis)
# Correct propensity score model
# Distribution balancing weighting with normalization and
# without regularization
fitdbwc <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
fitdbwc
summary(fitdbwc)
# Covariate balancing weighting function without regularization
fitcbwc <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "cb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitcbwc)
# Entropy balancing weighting function without regularization
fitebwc <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "eb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitebwc)
# Standard logistic regression
fitmlec <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlec)
# Distribution balancing weighting without normalization and
# without regularization
fitdbwcnn <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwcnn)
# Misspecified propensity score model
# Distribution balancing weighting with normalization and
# without regularization
fitdbwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwm)
# Covariate balancing weighting function without regularization
fitcbwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "cb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitcbwm)
# Entropy balancing weighting function without regularization
fitebwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "eb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitebwm)
# Standard logistic regression
fitmlem <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlem)
# Distribution balancing weighting without normalization and
# without regularization
fitdbwmnn <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwmnn)
# Distribution balancing weighting with normalization and
# with regularization
# Standardization
res_std_comp <- std_comp(formula_y = formula_y,
formula_ps = formula_ps_m,
estimand = "ATE", method_y = "wls",
data = df, std = TRUE,
weights = NULL)
# Estimation
fitdbwmr <- dbw(formula_y = formula_y,
formula_ps = res_std_comp$formula_ps,
estimand = "ATE", method = "dbw", method_y = "wls",
data = res_std_comp$data, normalize = TRUE,
vcov = TRUE, lambda = 0.01,
weights = res_std_comp$weights, clevel = 0.95)
summary(fitdbwmr)
# Covariate balancing weighting function with an estimating equation
# for the original covariate balancing propensity score method
fitcbwmcmb <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATEcombined", method = "cb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitcbwmcmb)
# Formula for a misspecified outcome model for the GAM
library(mgcv)
formula_y_gam <- stats::as.formula(y ~ s(x1mis) + s(x2mis) +
s(x3mis)+ s(x4mis))
# Distribution balancing weighting with the GAM
fitdbwmg <- dbw(formula_y = formula_y_gam, formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "gam", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwmg)
# Binary outcome case
# Empirically correct ATE
ybinom_t <- (y + (1 - treat) * 10 > 210) + 0
ybinom_c <- (y - treat * 10 > 210) + 0
ATEbinom <- mean(ybinom_t - ybinom_c)
ATEbinom
# Formula for a misspecified binary outcome model
formula_y_bin <- stats::as.formula(ybinom ~ x1mis + x2mis +
x3mis + x4mis)
# Distribution balancing weighting for the binary outcome
fitdbwmbin <- dbw(formula_y = formula_y_bin,
formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "logit", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwmbin)
# Standard logistic regression with the Horvitz-Thompson estimator
fitmlem_ht <- dbw(formula_y = y ~ 0, formula_ps = formula_ps_m,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlem_ht)
# Standard logistic regression with the Hajek estimator
fitmlem_hj <- dbw(formula_y = y ~ 1, formula_ps = formula_ps_m,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlem_hj)
dbw documentation built on Sept. 11, 2024, 6:50 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| dbw | R Documentation |
Doubly robust distribution balancing weighting (DBW) estimation
Description
dbw estimates a pre-specified parameter of interest (e.g.,
the average treatment effects (ATE) or the average treatment effects
on the treated (ATT)) with the augmented inverse probability weighting
(AIPW), where propensity scores are estimated using estimating
equations suitable for the parameter of interest and outcome models are
estimated using inverse probability weights. dbw can
also be used to estimate average outcomes (AO) in missing outcome cases.
Usage
dbw(
formula_y,
formula_ps,
estimand = "ATE",
method = "dbw",
method_y = "wls",
data,
normalize = TRUE,
vcov = TRUE,
lambda = 0,
weights = NULL,
clevel = 0.95,
tol = 1e-10,
init_lambda = 0.01
)
Arguments
formula_y |
an object of class |
formula_ps |
an object of class |
estimand |
a character string specifying a parameter of interest. Choose
"ATT" for the average treatment effects on the treated estimation, "ATE"
for the average treatment effects estimation, "ATC" for the average
outcomes estimation in missing outcome cases. You can choose "ATEcombined"
for the combined estimation for the average treatment effects estimation
when using the covariate balancing weighting ( |
method |
a character string specifying a method for propensity score estimation. Choose "dbw" for the distribution balancing weighting, "cb" for the covariate balancing weighting, "eb" for the entropy balancing weighting, and "mle" for the logistic regression with the maximum likelihood estimation. |
method_y |
a character string specifying a method for potential outcome
prediction. Choose "wls" for the linear model, "logit" for the logistic
regression, "gam" for the generalized additive model for the continuous
outcome, and "gambinom" for the generalized additive model for the binary
outcome. Note that variance-covariance matrix is calculated only when
|
data |
a data frame (or one that can be coerced to that class) containing the outcomes and the variables in the model. |
normalize |
a logical parameter indicating whether to normalize the estimated
weights to sum up to one for each treatment group for |
vcov |
a logical parameter indicating whether to estimate the variance. Default is TRUE. |
lambda |
a parameter taking 0 or larger specifying the degree of the
L2-regularization for propensity score estimation. |
weights |
an optional vector of ‘prior weights’ (e.g. sampling weights) to be used in the fitting process. Should be NULL or a numeric vector. |
clevel |
confidence levels. Default is 0.95. |
tol |
a tolerance parameter for |
init_lambda |
a parameter for |
Details
The treatment variable (or, response variable in missing outcome cases) must be binary and coded as 0 (for controlled or missing observations) or 1 (for treated or non-missing observations).
When the data frame has incomplete cases, which have NAs for either of
the treatment variable, the outcome variable, or explanatory variables
either for propensity score or outcome model estimation, dbw conducts
listwise deletion. Returned values (e.g., est_weights, ps, data)
do not contain values for these deleted cases.
For propensity score estimation, dbw can utilize the distribution
balancing weighting (method = "dbw"), covariate balancing weighting
(method = "cb"), entropy balancing weighting (method = "eb"), or
standard maximum likelihood estimation (method = "mle"). For the
covariate balancing weighting and entropy balancing weighting, dbw runs
much faster than the original functions (CBPS and ebalance)
by using loss-function-based algorithms, which also results in more
accurate covariate balance. For the ATT and ATC estimation, the distribution
balancing weighting, covariate balancing weighting, and entropy balancing
weighting are theoretically equivalent and dbw implements accordingly.
The parameter of interest is estimated by the AIPW estimator, where inverse
probability weights are standardized within each treatment group by being
devided by the size of the group after being calculated as
t_i / \pi_i - (1 - t_i) / (1 - \pi_i) for the ATE estimation,
(t_i - \pi_i) / (1 - \pi_i) for the ATT estimation,
(t_i - \pi_i) / \pi_i for the ATC estimation, and
t_i / \pi_i for the missing outcome cases. The resulting inverse probability
weights sum to 1 for the distribution balancing weighting, covariate
balancing weighting, and entropy balancing weighting estimators without
regularization.
The variance-covariance matrix for the parameter of interest and ancillary parameters is calculated using the sandwich variance formula obtained in the M-estimation framework.
When using regularization for propensity score estimation (lambda > 0),
you should standardize the covariates for propensity score estimation
by std_comp before using dbw. See example below for more details.
For the ATE estimation, it is recommended to specify the estimand as
"ATE", but you may specify it as "ATEcombined" when using the
covariate balancing weighting. The former utilizes the separated propensity
score estimation whereas the latter utilizes the combined estimation, and
the former should produce smaller biases and variances. Note that the
former estimates two propensity scores for each observation by estimating
two propensity score functions with different estimating equations.
For the AO estimation, NA values for the outcome variable for missing cases (the response variable taking "0") are not deleted. For this processing, the outcome variable name must not contain spaces.
Value
dbw returns an object of "dbw" class.
The function summary (i.e., summary.dbw) can be used to obtain or print a
summary of the results.
An object of class "dbw" is a list containing the following components:
est |
the point estimate of the parameter of interest. |
coef_ps |
a named vector of coefficients for propensity score estimation.
A list of two sets of coefficients for two sets of propensity scores
(one for estimating |
coef_y |
a named vector of coefficients for outcome model estimation.
A list of two sets of coefficients for two sets of outcome models
(one for estimating |
varcov |
the variance-covariance matrix of the coefficients and the parameter of interest. |
est_weights |
the estimated inverse probability weights. |
ps |
the estimated propensity scores. A list of two sets of the
estimated propensity scores (one for estimating |
predicted_y |
the predicted outcomes. A list of two sets of the
predicted outcomes (one for estimating |
converged |
logical. Were the propensity score estimation algorithms judged to have converged? |
effn |
the effective sample size for the parameter of interest estimation. |
effn_original |
the effective sample size with the initial weights. |
estimand |
the parameter of interest specified. |
method |
the method for propensity score estimation specified. |
method_y |
the method for outcome model estimation specified. |
response |
the treatment vector. The response (non-missingness) vector when the missing outcome cases. |
outcome |
the outcome vector. |
original_weights |
the weights initially supplied, a vector of 1s if none were. |
ci |
a matrix of the confidence intervals for the parameter of interest. |
formula_y |
the outcome model formula specified. |
formula_ps |
the propensity score model formula specified. |
call |
the matched call. |
data |
the data argument. |
normalize |
a logical argument indicating whether to normalize the
estimated weights for each treatment group for |
lambda |
the parameter specifying the degree of the L2-regularization. |
Author(s)
Hiroto Katsumata
References
Katsumata, Hiroto. 2024. "How Should We Estimate Inverse Probability Weights with Possibly Misspecified Propensity Score Models?" Political Science Research and Methods.
Imai, Kosuke and Marc Ratkovic. 2014. "Covariate Balancing Propensity Score." Journal of the Royal Statistical Society, Series B (Statistical Methodology) 76 (1): 243–63.
Hainmueller, Jens. 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies." Political Analysis 20 (1): 25–46.
See Also
summary.dbw, std_comp, gam
Examples
# Simulation from Kang and Shafer (2007) and Imai and Ratkovic (2014)
# ATE estimation
# True ATE is 10
tau <- 10
set.seed(12345)
n <- 1000
X <- matrix(stats::rnorm(n * 4, mean = 0, sd = 1), nrow = n, ncol = 4)
prop <- 1 / (1 + exp(X[, 1] - 0.5 * X[, 2] +
0.25 * X[, 3] + 0.1 * X[, 4]))
treat <- rbinom(n, 1, prop)
y <- 210 +
27.4 * X[, 1] + 13.7 * X[, 2] + 13.7 * X[, 3] + 13.7 * X[, 4] +
tau * treat + stats::rnorm(n = n, mean = 0, sd = 1)
ybinom <- (y > 210) + 0
df0 <- data.frame(X, treat, y, ybinom)
colnames(df0) <- c("x1", "x2", "x3", "x4", "treat", "y", "ybinom")
# Variables for a misspecified model
Xmis <- data.frame(x1mis = exp(X[, 1] / 2),
x2mis = X[, 2] * (1 + exp(X[, 1]))^(-1) + 10,
x3mis = (X[, 1] * X[, 3] / 25 + 0.6)^3,
x4mis = (X[, 2] + X[, 4] + 20)^2)
# Data frame and formulas for propensity score estimation
df <- data.frame(df0, Xmis)
formula_ps_c <- stats::as.formula(treat ~ x1 + x2 + x3 + x4)
formula_ps_m <- stats::as.formula(treat ~ x1mis + x2mis +
x3mis + x4mis)
# Formula for a misspecified outcome model
formula_y <- stats::as.formula(y ~ x1mis + x2mis + x3mis + x4mis)
# Correct propensity score model
# Distribution balancing weighting with normalization and
# without regularization
fitdbwc <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
fitdbwc
summary(fitdbwc)
# Covariate balancing weighting function without regularization
fitcbwc <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "cb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitcbwc)
# Entropy balancing weighting function without regularization
fitebwc <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "eb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitebwc)
# Standard logistic regression
fitmlec <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlec)
# Distribution balancing weighting without normalization and
# without regularization
fitdbwcnn <- dbw(formula_y = formula_y, formula_ps = formula_ps_c,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwcnn)
# Misspecified propensity score model
# Distribution balancing weighting with normalization and
# without regularization
fitdbwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwm)
# Covariate balancing weighting function without regularization
fitcbwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "cb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitcbwm)
# Entropy balancing weighting function without regularization
fitebwm <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "eb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitebwm)
# Standard logistic regression
fitmlem <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlem)
# Distribution balancing weighting without normalization and
# without regularization
fitdbwmnn <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwmnn)
# Distribution balancing weighting with normalization and
# with regularization
# Standardization
res_std_comp <- std_comp(formula_y = formula_y,
formula_ps = formula_ps_m,
estimand = "ATE", method_y = "wls",
data = df, std = TRUE,
weights = NULL)
# Estimation
fitdbwmr <- dbw(formula_y = formula_y,
formula_ps = res_std_comp$formula_ps,
estimand = "ATE", method = "dbw", method_y = "wls",
data = res_std_comp$data, normalize = TRUE,
vcov = TRUE, lambda = 0.01,
weights = res_std_comp$weights, clevel = 0.95)
summary(fitdbwmr)
# Covariate balancing weighting function with an estimating equation
# for the original covariate balancing propensity score method
fitcbwmcmb <- dbw(formula_y = formula_y, formula_ps = formula_ps_m,
estimand = "ATEcombined", method = "cb",
method_y = "wls", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitcbwmcmb)
# Formula for a misspecified outcome model for the GAM
library(mgcv)
formula_y_gam <- stats::as.formula(y ~ s(x1mis) + s(x2mis) +
s(x3mis)+ s(x4mis))
# Distribution balancing weighting with the GAM
fitdbwmg <- dbw(formula_y = formula_y_gam, formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "gam", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwmg)
# Binary outcome case
# Empirically correct ATE
ybinom_t <- (y + (1 - treat) * 10 > 210) + 0
ybinom_c <- (y - treat * 10 > 210) + 0
ATEbinom <- mean(ybinom_t - ybinom_c)
ATEbinom
# Formula for a misspecified binary outcome model
formula_y_bin <- stats::as.formula(ybinom ~ x1mis + x2mis +
x3mis + x4mis)
# Distribution balancing weighting for the binary outcome
fitdbwmbin <- dbw(formula_y = formula_y_bin,
formula_ps = formula_ps_m,
estimand = "ATE", method = "dbw",
method_y = "logit", data = df, normalize = TRUE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitdbwmbin)
# Standard logistic regression with the Horvitz-Thompson estimator
fitmlem_ht <- dbw(formula_y = y ~ 0, formula_ps = formula_ps_m,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlem_ht)
# Standard logistic regression with the Hajek estimator
fitmlem_hj <- dbw(formula_y = y ~ 1, formula_ps = formula_ps_m,
estimand = "ATE", method = "mle",
method_y = "wls", data = df, normalize = FALSE,
vcov = TRUE, lambda = 0, weights = NULL,
clevel = 0.95)
summary(fitmlem_hj)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.