bayesianSynth: Create a Bayesian Synthetic Control Object Using Panel Data
In bsynth: Bayesian Synthetic Control
bayesianSynth R Documentation
Create a Bayesian Synthetic Control Object Using Panel Data
Description
A Bayesian Synthetic Control has raw data and draws from the posterior
distribution. This is represented by an R6 Class.
public methods:
-
initialize() initializes the variables and model parameters
-
fit() fits the stan model and returns a fit object
-
updateWidth updates the width of the credible interval
-
placeboPlot generates a counterfactual placebo plot
-
effectPlot returns a plot of the treatment effect over time
-
summarizeLiftreturns descriptive statistics of the lift estimate
-
biasDraws returns a plot of the relative bias in a LFM
-
liftDraws returns a plot of the posterior lift distribution
-
liftBias returns a plot of the relative bias given a lift offset
Data structure:
Value
vizdraws object with the relative bias with offset.
Active bindings
timeTilesggplot2 object that shows when
the intervention happened.
plotDatareturns tibble with the observed outcome and the
counterfactual data.
interventionTimereturns intervention time period (e.g., year)
in which the treatment occurred.
syntheticreturns ggplot2 object that shows the
observed and counterfactual outcomes over time.
checksreturns MCMC checks.
liftdraws from the posterior distribution of the lift.
Methods
Public methods
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Method new()
Create a new bayesianSynth object.
Usage
bayesianSynth$new(
data,
time,
id,
treated,
outcome,
ci_width = 0.75,
gp = FALSE,
covariates = NULL,
predictor_match = FALSE,
predictor_match_covariates0 = NULL,
predictor_match_covariates1 = NULL,
vs = NULL
)
Arguments
dataLong data.frame object with fields outcome, time, id,
and treatment indicator.
timeName of the variable in the data frame that
identifies the time period (e.g. year, month, week etc).
idName of the variable in the data frame that
identifies the units (e.g. country, region etc).
treatedName of the variable in the data frame that contains
the treatment assignment of the intervention.
outcomeName of the outcome variable.
ci_widthCredible interval's width. This number is in the
(0,1) interval.
gpLogical that indicates whether or not to include a
Gaussian Process as part of the model.
covariatesData.frame with time dependent covariates for
for each unit and time field.
Defaults to NULL if no covariates should be included in the model.
predictor_matchLogical that indicates whether or not to run
the matching version of the Bayesian Synthetic Control. This option can
not be used with gp, covariates or multiple treated units.
predictor_match_covariates0data.frame with time independent
covariates on each row and column indicating the control unit names
(dim k x J+1).
predictor_match_covariates1Vector with time independent
covariates for the treated unit (dim k x 1).
vsVector of weights for the importance of the predictors used
in creating the synthetic control.
Defaults to equal weight for all predictors.
Returns
A new bayesianSynth object.
Method fit()
Fit Stan model.
Usage
bayesianSynth$fit(...)
Arguments
...other arguments passed to rstan::sampling().
Method updateWidth()
Update the width of the credible interval.
Usage
bayesianSynth$updateWidth(ci_width = 0.75)
Arguments
ci_widthNew width for the credible interval. This number should
be in the (0,1) interval.
Method summarizeLift()
returns descriptive statistics of the lift estimate.
Usage
bayesianSynth$summarizeLift()
Method effectPlot()
effect ggplot2 object that shows the
effect of the intervention over time.
Usage
bayesianSynth$effectPlot(facet = TRUE, subset = NULL)
Arguments
facetBoolean that is TRUE if we want to divide the plot for each
unit.
subsetSet of units to use in the effect plot.
Method placeboPlot()
Plot placebo intervention.
Usage
bayesianSynth$placeboPlot(periods, ...)
Arguments
periodsPositive number of periods for the placebo intervention.
...other arguments passed to rstan::sampling().
Returns
ggplot2 object for placebo treatment effect.
Method biasDraws()
Plots relative upper bias / tau for a time period (firstT, lastT).
Usage
bayesianSynth$biasDraws(small_bias = 0.3, firstT, lastT)
Arguments
small_biasThreshold value for considering the bias "small".
firstTStart of the time period to compute relative bias over.
Must be after the intervention.
lastTEnd of the time period to compute relative bias over.
Must be after the intervention.
Returns
vizdraw object with the posterior distribution of relative bias.
Bias is scaled by the time periods.
Method liftDraws()
Plots lift.
Usage
bayesianSynth$liftDraws(from, to, ...)
Arguments
fromFirst period to consider when calculating lift. If infinite,
set to the time of the intervention.
toLast period to consider when calculating lift. If infinite, set
to the last period.
...other arguments passed to vizdraws::vizdraws().
Returns
vizdraws object with the posterior distribution of the lift.
Method liftBias()
Plot Bias magnitude in terms of lift for period (firstT, lastT)
pre_MADs / y0 relative to lift thresholds.
Usage
bayesianSynth$liftBias(firstT, lastT, offset, ...)
Arguments
firstTstart of the time period to compute relative bias
over. They must be after the intervention.
lastTend of the Time period to compute relative bias
over. They must be after the intervention.
offsetTarget lift %.
...other arguments passed to vizdraws::vizdraws().
Method weightDraws()
Plot implicit weight distribution across draws.
Usage
bayesianSynth$weightDraws()
Returns
ggplot object with weight distribution per unit.
Method weightCorr()
Plots correlations between weights across draws.
Usage
bayesianSynth$weightCorr()
Returns
ggplot heatmap object with correlations.
Method clone()
The objects of this class are cloneable with this method.
Usage
bayesianSynth$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
bsynth documentation built on June 25, 2024, 5:08 p.m.
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| bayesianSynth | R Documentation |
Create a Bayesian Synthetic Control Object Using Panel Data
Description
A Bayesian Synthetic Control has raw data and draws from the posterior distribution. This is represented by an R6 Class.
public methods:
-
initialize()initializes the variables and model parameters -
fit()fits the stan model and returns a fit object -
updateWidthupdates the width of the credible interval -
placeboPlotgenerates a counterfactual placebo plot -
effectPlotreturns a plot of the treatment effect over time -
summarizeLiftreturns descriptive statistics of the lift estimate -
biasDrawsreturns a plot of the relative bias in a LFM -
liftDrawsreturns a plot of the posterior lift distribution -
liftBiasreturns a plot of the relative bias given a lift offset Data structure:
Value
vizdraws object with the relative bias with offset.
Active bindings
timeTilesggplot2 object that shows when the intervention happened.
plotDatareturns tibble with the observed outcome and the counterfactual data.
interventionTimereturns intervention time period (e.g., year) in which the treatment occurred.
syntheticreturns ggplot2 object that shows the observed and counterfactual outcomes over time.
checksreturns MCMC checks.
liftdraws from the posterior distribution of the lift.
Methods
Public methods
Method new()
Create a new bayesianSynth object.
Usage
bayesianSynth$new( data, time, id, treated, outcome, ci_width = 0.75, gp = FALSE, covariates = NULL, predictor_match = FALSE, predictor_match_covariates0 = NULL, predictor_match_covariates1 = NULL, vs = NULL )
Arguments
dataLong data.frame object with fields outcome, time, id, and treatment indicator.
timeName of the variable in the data frame that identifies the time period (e.g. year, month, week etc).
idName of the variable in the data frame that identifies the units (e.g. country, region etc).
treatedName of the variable in the data frame that contains the treatment assignment of the intervention.
outcomeName of the outcome variable.
ci_widthCredible interval's width. This number is in the (0,1) interval.
gpLogical that indicates whether or not to include a Gaussian Process as part of the model.
covariatesData.frame with time dependent covariates for for each unit and time field. Defaults to NULL if no covariates should be included in the model.
predictor_matchLogical that indicates whether or not to run the matching version of the Bayesian Synthetic Control. This option can not be used with gp, covariates or multiple treated units.
predictor_match_covariates0data.frame with time independent covariates on each row and column indicating the control unit names (dim k x J+1).
predictor_match_covariates1Vector with time independent covariates for the treated unit (dim k x 1).
vsVector of weights for the importance of the predictors used in creating the synthetic control. Defaults to equal weight for all predictors.
Returns
A new bayesianSynth object.
Method fit()
Fit Stan model.
Usage
bayesianSynth$fit(...)
Arguments
...other arguments passed to
rstan::sampling().
Method updateWidth()
Update the width of the credible interval.
Usage
bayesianSynth$updateWidth(ci_width = 0.75)
Arguments
ci_widthNew width for the credible interval. This number should be in the (0,1) interval.
Method summarizeLift()
returns descriptive statistics of the lift estimate.
Usage
bayesianSynth$summarizeLift()
Method effectPlot()
effect ggplot2 object that shows the effect of the intervention over time.
Usage
bayesianSynth$effectPlot(facet = TRUE, subset = NULL)
Arguments
facetBoolean that is TRUE if we want to divide the plot for each unit.
subsetSet of units to use in the effect plot.
Method placeboPlot()
Plot placebo intervention.
Usage
bayesianSynth$placeboPlot(periods, ...)
Arguments
periodsPositive number of periods for the placebo intervention.
...other arguments passed to
rstan::sampling().
Returns
ggplot2 object for placebo treatment effect.
Method biasDraws()
Plots relative upper bias / tau for a time period (firstT, lastT).
Usage
bayesianSynth$biasDraws(small_bias = 0.3, firstT, lastT)
Arguments
small_biasThreshold value for considering the bias "small".
firstTStart of the time period to compute relative bias over. Must be after the intervention.
lastTEnd of the time period to compute relative bias over. Must be after the intervention.
Returns
vizdraw object with the posterior distribution of relative bias. Bias is scaled by the time periods.
Method liftDraws()
Plots lift.
Usage
bayesianSynth$liftDraws(from, to, ...)
Arguments
fromFirst period to consider when calculating lift. If infinite, set to the time of the intervention.
toLast period to consider when calculating lift. If infinite, set to the last period.
...other arguments passed to vizdraws::vizdraws().
Returns
vizdraws object with the posterior distribution of the lift.
Method liftBias()
Plot Bias magnitude in terms of lift for period (firstT, lastT) pre_MADs / y0 relative to lift thresholds.
Usage
bayesianSynth$liftBias(firstT, lastT, offset, ...)
Arguments
firstTstart of the time period to compute relative bias over. They must be after the intervention.
lastTend of the Time period to compute relative bias over. They must be after the intervention.
offsetTarget lift %.
...other arguments passed to vizdraws::vizdraws().
Method weightDraws()
Plot implicit weight distribution across draws.
Usage
bayesianSynth$weightDraws()
Returns
ggplot object with weight distribution per unit.
Method weightCorr()
Plots correlations between weights across draws.
Usage
bayesianSynth$weightCorr()
Returns
ggplot heatmap object with correlations.
Method clone()
The objects of this class are cloneable with this method.
Usage
bayesianSynth$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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