Lalonde dataset

Description

Lalonde dataset

Usage

lalonde

Format

An object of class data.frame with 445 rows and 12 columns.

Source

https://CRAN.R-project.org/package=arm

Plot the histogram or density of the Conditional Average Treatment Effect

Description

Plot the conditional average treatment effect (CATE) of a 'bartCause' model. The conditional average treatment effect is derived from taking the difference between predictions for each individual under the control condition and under the treatment condition averaged over the population. Means of the CATE distribution will resemble SATE and PATE but the CATE distribution accounts for more uncertainty than SATE and less uncertainty than PATE.

Usage

plot_CATE(
  .model,
  type = c("histogram", "density"),
  ci_80 = FALSE,
  ci_95 = FALSE,
  reference = NULL,
  .mean = FALSE,
  .median = FALSE
)

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSup.rule = 'none'
)
plot_CATE(model_results)

Plot Individual Conditional Average Treatment effects

Description

Plots a histogram of Individual Conditional Average Treatment effects (ICATE). ICATEs are the difference in each individual's predicted outcome under the treatment and predicted outcome under the control averaged over the individual. Plots of ICATEs are useful to identify potential heterogeneous treatment effects between different individuals. ICATE plots can be grouped by discrete variables.

Usage

plot_ICATE(.model, .group_by = NULL, n_bins = 30, .alpha = 0.7)

Arguments

Value

ggplot object

Author(s)

George Perrett

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSup.rule = 'none'
)
plot_ICATE(model_results, lalonde$married)

Plot histogram or density of Population Average Treatment Effect

Description

Plot shows the Population Average Treatment Effect which is derived from the posterior predictive distribution of the difference between y | z=1, X and y | z=0, X. Mean of PATE will resemble CATE and SATE but PATE will account for more uncertainty and is recommended for informing inferences on the average treatment effect.

Usage

plot_PATE(
  .model,
  type = c("histogram", "density"),
  ci_80 = FALSE,
  ci_95 = FALSE,
  reference = NULL,
  .mean = FALSE,
  .median = FALSE
)

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSup.rule = 'none'
)
plot_PATE(model_results)

Plot histogram or density of Sample Average Treatment Effects

Description

Plot a histogram or density of the Sample Average Treatment Effect (SATE). The Sample Average Treatment Effect is derived from taking the difference of each individual's observed outcome and a predicted counterfactual outcome from a BART model averaged over the population. The mean of SATE will resemble means of CATE and PATE but will account for the least uncertainty.

Usage

plot_SATE(
  .model,
  type = c("histogram", "density"),
  ci_80 = FALSE,
  ci_95 = FALSE,
  reference = NULL,
  .mean = FALSE,
  .median = FALSE
)

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSup.rule = 'none'
)
plot_SATE(model_results)

Plot the balance

Description

Visualize balance of variables between treatment and control groups. Balance plot reflects balance in standardized units.

Usage

plot_balance(.data, treatment, confounders)

Arguments

Value

ggplot object

Author(s)

Joseph Marlo

Examples

data(lalonde)
plot_balance(lalonde, 'treat', c('re78', 'age', 'educ')) + labs(title = 'My new title')

Plot common support based on the standard deviation rule, chi squared rule, or both

Description

Plot common support based on the standard deviation rule, chi squared rule, or both.

Usage

plot_common_support(.model, rule = c("both", "sd", "chi"))

Arguments

Details

Sufficient overlap/common support is an assumption of causal inference. BART models use the uncertainty of counter factual uncertainty. When the posterior distribution of an individual's counterfactual prediction extends beyond a specified cut-point, that point likely has insufficient common support. 'bartCause' model offer the option to automatically remove points without common support from analyses, however, this must be specified during model fitting. Cut-points are determined through one of two rules: the standard deviation (sd) or chi-squared (chi). Under the standard deviation rule, a point has weak common support if its posterior distribution of the counterfactual deviation is greater than the maximum posterior of the observed predictions with 1 standard deviation of the distribution of standard deviations for each individual's predicted outcome under the observed assignment. Under the chi-squared rule, a point is discarded if the variance between its counterfactual prediction over observed prediction are statistically different under a chi-squared distribution with 1 degree of freedom. For more details on discard rules see Hill and Su 2013.

When called this plot will show how many points would have been removed under the standard deviation and chi-squared rules. This plot should be used as a diagnostic for 'bartCause' models fit without a common-support rule.

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

References

Hill, J., & Su, Y. S. (2013). Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes. The Annals of Applied Statistics, 1386-1420.

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none'
)
plot_common_support(model_results)

LOESS plot of a continuous moderating variable

Description

Plot the LOESS prediction of ICATEs by a continuous covariate. This is an alternative to partial dependency plots to assess treatment effect heterogeneity by a continuous covariate. See Carnegie, Dorie and Hill 2019.

Usage

plot_moderator_c_loess(.model, moderator, line_color = "blue")

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

References

Carnegie, N., Dorie, V., & Hill, J. L. (2019). Examining treatment effect heterogeneity using BART. Observational Studies, 5(2), 52-70.

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none'
)
plot_moderator_c_loess(model_results, lalonde$age)

Partial dependency plot of a continuous moderating variable

Description

Plot a partial dependency plot with a continuous covariate from a 'bartCause' model. Identify treatment effect variation predicted across levels of a continuous variable.

Usage

plot_moderator_c_pd(.model, moderator, n_bins = NULL)

Arguments

Details

Partial dependency plots are one way to evaluate heterogeneous treatment effects that vary by values of a continuous covariate. For more information on partial dependency plots from BART causal inference models see Green and Kern 2012.

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

References

Green, D. P., & Kern, H. L. (2012). Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public opinion quarterly, 76(3), 491-511.

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none',
 keepTrees = TRUE
)
plot_moderator_c_pd(model_results, lalonde$age)

Plot the Conditional Average Treatment Effect conditional on a discrete moderator

Description

Plot the Conditional Average Treatment Effect split by a discrete moderating variable. This plot will provide a visual test of moderation by discrete variables.

Usage

plot_moderator_d_density(
  .model,
  moderator,
  .alpha = 0.7,
  facet = FALSE,
  .ncol = 1
)

Arguments

Value

ggplot object

Author(s)

George Perrett

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none'
)
plot_moderator_d_density(model_results, lalonde$educ)

Plot the posterior interval of the Conditional Average Treatment Effect grouped by a discrete variable

Description

Plots the range of the Conditional Average Treatment Effect grouped by a discrete variable. This is analogous to plot_moderator_d_density but is preferable for moderators with many categories. Rather than plotting the full density, the posterior range is shown.

Usage

plot_moderator_d_linerange(.model, moderator, .alpha = 0.7, horizontal = FALSE)

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none'
)
plot_moderator_d_linerange(model_results, lalonde$educ)

Plot a single regression tree of covariates on ICATEs

Description

Plot a single regression tree for exploratory heterogeneous effects. Fit single regression tree on bartc() ICATEs to produce variable importance plot. This plot is useful for identifying potential moderating variables. Tree depth may be set to depths 1, 2 or 3. Terminal nodes signal the Conditional Average Treatment effect within levels of moderation variables. Trees with different values across terminal nodes suggest strong treatment effect moderation.

Usage

plot_moderator_search(.model, max_depth = c(2, 1, 3))

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none'
)
plot_moderator_search(model_results)

Plot the overlap via propensity score method

Description

Plot histograms showing the overlap between propensity scores by treatment status.

Usage

plot_overlap_pScores(
  .data,
  treatment,
  response,
  confounders,
  plot_type = c("histogram", "density"),
  pscores = NULL,
  ...
)

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

See Also

plot_overlap_vars

Examples

data(lalonde)
plot_overlap_pScores(
 .data = lalonde,
 treatment = 'treat',
 response = 're78',
 confounders = c('age', 'educ'),
 plot_type = 'histogram',
 pscores = NULL,
 seed = 44
)

Plot the overlap of variables

Description

Plot histograms showing the overlap between variables by treatment status.

Usage

plot_overlap_vars(
  .data,
  treatment,
  confounders,
  plot_type = c("histogram", "density")
)

Arguments

Value

ggplot object

Author(s)

George Perrett, Joseph Marlo

See Also

plot_overlap_pScores

Examples

data(lalonde)
plot_overlap_vars(
 .data = lalonde,
 treatment = 'treat',
 confounders = c('age', 'educ'),
 plot_type = 'Histogram'
)

Trace plot the estimands of a 'bartCause::bartc()' model

Description

Returns a ggplot of the estimated effect over each iteration of the model fit. This is used to visually assess the convergence of Markov chain Monte Carlo (MCMC) sampling. Chains should be well mixed such that no single color is notably separate from others.

Usage

plot_trace(.model)

Arguments

Value

ggplot object

Author(s)

Joseph Marlo, George Perrett

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSup.rule = 'none'
)
plot_trace(.model = model_results)

Plot a waterfall of the ICATEs

Description

Plots the point and posterior intervals of each individual's ICATE ordered by the ICATE or a continuous variable. Points can be colored by a discrete variable. Waterfall plots are a useful visual diagnostic of possible treatment effect heterogeneity. A flat line implies little treatment effect heterogeneity while a steeper curve implies that the treatment effect varies across individuals in the sample. Ordering points by a continuous variable or coloring points by a discrete variable can be helpful to identify potential moderators of the treatment effect.

Usage

plot_waterfall(
  .model,
  descending = TRUE,
  .order = NULL,
  .color = NULL,
  .alpha = 0.5
)

Arguments

Value

ggplot object

Author(s)

George Perrett

Examples

data(lalonde)
confounders <- c('age', 'educ', 'black', 'hisp', 'married', 'nodegr')
model_results <- bartCause::bartc(
 response = lalonde[['re78']],
 treatment = lalonde[['treat']],
 confounders = as.matrix(lalonde[, confounders]),
 estimand = 'ate',
 commonSuprule = 'none'
)
plot_waterfall(model_results)