Description

Residual Balancing Weights for Marginal Structural Models.

Description

Residual balancing is a robust method of constructing weights for marginal structural models, which can be used to estimate (a) the average treatment effect in a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(), that produce residual balancing weights for estimating (a), (b), (c), respectively.

README.md

cran.r-project.org

rbw: Residual Balancing Weights for Marginal Structural Models

Residual balancing is a method of constructing weights for marginal structural models, which can be used to estimate marginal effects of time-varying treatments and controlled direct/mediator effects in causal mediation analysis. Compared with inverse probability-of-treatment weights (IPW), residual balancing weights tend to be more robust and more efficient, and are easier to use with continuous exposures. This package provides three main functions, rbwPoint(), rbwPanel() and rbwMed(), that produce residual balancing weights for analyzing point treatments, time-varying treatments, and causal mediation, respectively.

Reference

Zhou, Xiang and Geoffrey T Wodtke. 2020. “Residual Balancing: A Method of Constructing Weights for Marginal Structural Models” Political Analysis.

Installation

You can install the released version of rbw from CRAN with:

install.packages("rbw")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("xiangzhou09/rbw")

Estimating the Average Effect of a Point Treatment

The rbwPoint() function constructs residual balancing weights for estimating the average effect of a point treatment. The following example illustrates its use by estimating the average effect of televised political advertisements (treat) on campaign contributions (Cont) among 16,265 zipcodes in the 2004 and 2008 US presidential elections.

library(rbw)
# install.packages("survey")
library(survey)

# residual balancing weights
rbwPoint_fit <- rbwPoint(treat, baseline_x = c(log_TotalPop, PercentOver65, log_Inc, PercentHispanic, PercentBlack, density, per_collegegrads, CanCommute), data = advertisement)
#> Entropy minimization converged within tolerance level

# attach residual balancing weights to data
advertisement$rbw_point <- rbwPoint_fit$weights

# fit marginal structural model
rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw_point, data = advertisement)

# the outcome model includes the treatment, the square of the treatment,
# and state-level fixed effects (Fong, Hazlett, and Imai 2018)
msm_rbwPoint <- svyglm(Cont ~ treat + I(treat^2) + factor(StFIPS), design = rbw_design)
summary(msm_rbwPoint)
#> 
#> Call:
#> svyglm(formula = Cont ~ treat + I(treat^2) + factor(StFIPS), 
#>     design = rbw_design)
#> 
#> Survey design:
#> svydesign(ids = ~1, weights = ~rbw_point, data = advertisement)
#> 
#> Coefficients:
#>                  Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)       3.57549    2.79045   1.281 0.200095    
#> treat             0.43986    1.70909   0.257 0.796901    
#> I(treat^2)        0.01552    0.16555   0.094 0.925332    
#> factor(StFIPS)5  -1.25821    1.07914  -1.166 0.243656    
#> factor(StFIPS)6  64.54555    5.42773  11.892  < 2e-16 ***
#> factor(StFIPS)10 12.25245    6.06566   2.020 0.043403 *  
#> factor(StFIPS)13 11.14059    2.98022   3.738 0.000186 ***
#> factor(StFIPS)17 20.98707    4.37924   4.792 1.66e-06 ***
#> factor(StFIPS)20 -1.89078    1.17473  -1.610 0.107516    
#> factor(StFIPS)21 -1.75188    1.21534  -1.441 0.149469    
#> factor(StFIPS)23 -2.07515    1.60297  -1.295 0.195489    
#> factor(StFIPS)24 36.79553    8.26927   4.450 8.66e-06 ***
#> factor(StFIPS)25 48.39716    7.34165   6.592 4.47e-11 ***
#> factor(StFIPS)27  2.31899    2.11116   1.098 0.272027    
#> factor(StFIPS)28 -0.11943    1.25105  -0.095 0.923948    
#> factor(StFIPS)30 -4.49525    1.58284  -2.840 0.004517 ** 
#> factor(StFIPS)31 -3.16796    1.00206  -3.161 0.001573 ** 
#> factor(StFIPS)34 23.32090    4.04985   5.758 8.64e-09 ***
#> factor(StFIPS)36 29.47346    4.29735   6.859 7.21e-12 ***
#> factor(StFIPS)40  0.58593    1.16360   0.504 0.614588    
#> factor(StFIPS)45  1.14183    1.46973   0.777 0.437230    
#> factor(StFIPS)46 -4.75496    1.99265  -2.386 0.017033 *  
#> factor(StFIPS)47  5.66276    1.84947   3.062 0.002203 ** 
#> factor(StFIPS)48 18.32801    2.37261   7.725 1.19e-14 ***
#> factor(StFIPS)50 -0.50451    1.93071  -0.261 0.793860    
#> factor(StFIPS)56  2.17016    3.10951   0.698 0.485244    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 13738.91)
#> 
#> Number of Fisher Scoring iterations: 2

Estimating Marginal Effects of Time-varying Treatments

The rbwPanel() function constructs residual balancing weights for estimating marginal effects of time-varying treatments. The following example illustrates its use by estimating the effect of negative campaign advertising (d.gone.neg) on election outcomes (demprcnt) for 113 Democratic candidates in US Senate and Gubernatorial elections.


# models for time-varying confounders
m1 <- lm(dem.polls ~ (d.gone.neg.l1 + dem.polls.l1 + undother.l1) * factor(week), data = campaign_long)
m2 <- lm(undother ~ (d.gone.neg.l1 + dem.polls.l1 + undother.l1) * factor(week), data = campaign_long)
xmodels <- list(m1, m2)

# residual balancing weights
rbwPanel_fit <- rbwPanel(treatment = d.gone.neg, xmodels = xmodels, id = id, time = week, data = campaign_long)
#> Entropy minimization converged within tolerance level

# merge weights into wide-format data
campaign_wide2 <- merge(campaign_wide, rbwPanel_fit$weights, by = "id")

# fit a marginal structural model (adjusting for baseline confounders)
rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw, data = campaign_wide2)
msm_rbw <- svyglm(demprcnt ~ cum_neg * deminc + camp.length + factor(year) + office, design = rbw_design)
summary(msm_rbw)
#> 
#> Call:
#> svyglm(formula = demprcnt ~ cum_neg * deminc + camp.length + 
#>     factor(year) + office, design = rbw_design)
#> 
#> Survey design:
#> svydesign(ids = ~1, weights = ~rbw, data = campaign_wide2)
#> 
#> Coefficients:
#>                  Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)      50.39769    2.52269  19.978  < 2e-16 ***
#> cum_neg           0.96579    0.45496   2.123 0.036143 *  
#> deminc           17.04229    2.66426   6.397 4.62e-09 ***
#> camp.length      -0.09085    0.06175  -1.471 0.144222    
#> factor(year)2002 -5.57359    1.53081  -3.641 0.000425 ***
#> factor(year)2004 -6.22630    1.67340  -3.721 0.000322 ***
#> factor(year)2006 -1.51220    1.93697  -0.781 0.436751    
#> office            0.02811    1.11988   0.025 0.980026    
#> cum_neg:deminc   -2.99678    0.65932  -4.545 1.49e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 25.21453)
#> 
#> Number of Fisher Scoring iterations: 2

Estimating Controlled Direct Effects (CDE)

In causal mediation analysis, the rbwMed() function can be used to construct residual balancing weights for estimating the controlled direct effect or the controlled mediator effect with a marginal structural model. The following example illustrates its use by estimating the controlled direct effect of shared democracy (democ) on public support for war (strike) at different levels of perceived morality of war (immoral) for a sample of respondents in a survey experiment.

# models for post-treatment confounders
m1 <- lm(threatc ~ ally + trade + h1 + i1 + p1 + e1 + r1 +
  male + white + age + ed4 + democ, data = peace)

m2 <- lm(cost ~ ally + trade + h1 + i1 + p1 + e1 + r1 +
  male + white + age + ed4 + democ, data = peace)

m3 <- lm(successc ~ ally + trade + h1 + i1 + p1 + e1 + r1 +
  male + white + age + ed4 + democ, data = peace)

# residual balancing weights
rbwMed_fit <- rbwMed(treatment = democ, mediator = immoral,
  zmodels = list(m1, m2, m3), interact = TRUE,
  baseline_x = c(ally, trade, h1, i1, p1, e1, r1, male, white, age, ed4),
  data = peace)
#> Entropy minimization converged within tolerance level

# attach residual balancing weights to data
peace$rbw_cde <- rbwMed_fit$weights

# fit marginal structural model
rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw_cde, data = peace)
msm_rbwMed <- svyglm(strike ~ democ * immoral, design = rbw_design)
summary(msm_rbwMed)
#> 
#> Call:
#> svyglm(formula = strike ~ democ * immoral, design = rbw_design)
#> 
#> Survey design:
#> svydesign(ids = ~1, weights = ~rbw_cde, data = peace)
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)    2.74428    0.06255  43.875  < 2e-16 ***
#> democ         -0.37399    0.09893  -3.780 0.000164 ***
#> immoral       -1.36569    0.15082  -9.055  < 2e-16 ***
#> democ:immoral  0.09091    0.19782   0.460 0.645899    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 1.384994)
#> 
#> Number of Fisher Scoring iterations: 2

r-rbw

rbw: Residual Balancing Weights for Marginal Structural Models

Installation

Estimating the Average Effect of a Point Treatment

Estimating Marginal Effects of Time-varying Treatments

Estimating Controlled Direct Effects (CDE)

Version

License

Status

Source

Homepage

Platforms (75)

rbw: Residual Balancing Weights for Marginal Structural Models

Installation

Estimating the Average Effect of a Point Treatment

Estimating Marginal Effects of Time-varying Treatments

Estimating Controlled Direct Effects (CDE)

Version

License

Status

Source

Homepage

Platforms75 (75)

Platforms (75)