bayesianSynth {bsynth}

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

timeTiles

ggplot2 object that shows when the intervention happened.

plotData

returns tibble with the observed outcome and the counterfactual data.

interventionTime

returns intervention time period (e.g., year) in which the treatment occurred.

synthetic

returns ggplot2 object that shows the observed and counterfactual outcomes over time.

checks

returns MCMC checks.

lift

draws from the posterior distribution of the lift.

Methods

Public methods

• bayesianSynth$new()

• bayesianSynth$fit()

• bayesianSynth$updateWidth()

• bayesianSynth$summarizeLift()

• bayesianSynth$effectPlot()

• bayesianSynth$placeboPlot()

• bayesianSynth$biasDraws()

• bayesianSynth$liftDraws()

• bayesianSynth$liftBias()

• bayesianSynth$weightDraws()

• bayesianSynth$weightCorr()

• bayesianSynth$clone()

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

data

Long data.frame object with fields outcome, time, id, and treatment indicator.

time

Name of the variable in the data frame that identifies the time period (e.g. year, month, week etc).

id

Name of the variable in the data frame that identifies the units (e.g. country, region etc).

treated

Name of the variable in the data frame that contains the treatment assignment of the intervention.

outcome

Name of the outcome variable.

ci_width

Credible interval's width. This number is in the (0,1) interval.

gp

Logical that indicates whether or not to include a Gaussian Process as part of the model.

covariates

Data.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_match

Logical 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_covariates0

data.frame with time independent covariates on each row and column indicating the control unit names (dim k x J+1).

predictor_match_covariates1

Vector with time independent covariates for the treated unit (dim k x 1).

vs

Vector 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_width

New 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

facet

Boolean that is TRUE if we want to divide the plot for each unit.

subset

Set of units to use in the effect plot.

Method placeboPlot()

Plot placebo intervention.

Usage

bayesianSynth$placeboPlot(periods, ...)

Arguments

periods

Positive 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_bias

Threshold value for considering the bias "small".

firstT

Start of the time period to compute relative bias over. Must be after the intervention.

lastT

End 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

from

First period to consider when calculating lift. If infinite, set to the time of the intervention.

to

Last 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

firstT

start of the time period to compute relative bias over. They must be after the intervention.

lastT

end of the Time period to compute relative bias over. They must be after the intervention.

offset

Target 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

deep

Whether to make a deep clone.