multiple.synth {SCtools}

Function to Apply Synthetic Controls to Multiple Treated Units

Description

Generates one synthetic control for each treated unit and calculates the difference between the treated and the synthetic control for each. Returns a vector with outcome values for the synthetic controls, a plot of average treatment effects, and if required generates placebos out of the donor pool to be used in conjunction with plac.dist. All arguments are the same used for dataprep in the Synth package, except for treated.units, treatment.time, and generate.placebos.

Usage

multiple.synth(
  foo,
  predictors,
  predictors.op,
  dependent,
  unit.variable,
  time.variable,
  special.predictors,
  treated.units,
  control.units,
  time.predictors.prior,
  time.optimize.ssr,
  unit.names.variable,
  time.plot,
  treatment.time,
  gen.placebos = FALSE,
  strategy = "sequential",
  Sigf.ipop = 5
)

multiple_synth(
  foo,
  predictors,
  predictors.op,
  dependent,
  unit.variable,
  time.variable,
  special.predictors,
  treated.units,
  control.units,
  time.predictors.prior,
  time.optimize.ssr,
  unit.names.variable,
  time.plot,
  treatment.time,
  gen.placebos = FALSE,
  strategy = "sequential",
  Sigf.ipop = 5
)

Arguments

foo

Dataframe with the panel data.

predictors

Vector of column numbers or column-name character strings that identifies the predictors' columns. All predictors have to be numeric.

predictors.op

A character string identifying the method (operator) to be used on the predictors. Default is mean.

dependent

The column number or a string with the column name that corresponds to the dependent variable.

unit.variable

The column number or a string with the column name that identifies unit numbers. The variable must be numeric.

time.variable

The column number or a string with the column name that identifies the period (time) data. The variable must be numeric.

special.predictors

A list object identifying additional predictors and their pre-treatment years and operators.

treated.units

A vector identifying the unit.variable numbers of the treated units.

control.units

A vector identifying the unit.variable numbers of the control units.

time.predictors.prior

A numeric vector identifying the pretreatment periods over which the values for the outcome predictors should be averaged.

time.optimize.ssr

A numeric vector identifying the periods of the dependent variable over which the loss function should be minimized between each treated unit and its synthetic control.

unit.names.variable

The column number or string with column name identifying the variable with units' names. The variable must be a character.

time.plot

A vector identifying the periods over which results are to be plotted with path.plot

treatment.time

A numeric value with the value in time.variable that marks the intervention.

gen.placebos

Logical. Whether a placebo (a synthetic control) for each unit in the donor pool should be constructed. Will increase computation time.

strategy

The processing method you wish to use "sequential", "multicore" or "multisession" . Use "multicore" or "multisession" to parallelize operations and reduce computing time. Default is sequential. Since SCtools >= 0.3.2 "multiprocess" is deprecated.

Sigf.ipop

The Precision setting for the ipop optimization routine. Default of 5.

Details

The function runs dataprep and synth for each unit identified in treated.units. It saves the vector with predicted values for each synthetic control, to be used in estimating average treatment effects in applications of Synthetic Controls for multiple treated units.

For further details on the arguments, see the documentation of Synth.

Value

Data frame. Each column contains the outcome values for every time-point for one unit or its synthetic control. The last column contains the time-points.

Examples

## Using the toy data from 'Synth':

library(Synth)
data(synth.data)
set.seed(42)

multi <- multiple.synth(foo = synth.data,
                       predictors = c("X1"),
                       predictors.op = "mean",
                       dependent = "Y",
                       unit.variable = "unit.num",
                       time.variable = "year",
                       treatment.time = 1990,
                       special.predictors = list(
                         list("Y", 1991, "mean")
                       ),
                       treated.units = c(2,7),
                       control.units = c(29, 13, 17),
                       time.predictors.prior = c(1984:1989),
                       time.optimize.ssr = c(1984:1990),
                       unit.names.variable = "name",
                       time.plot = 1984:1996, gen.placebos =  FALSE, 
                       Sigf.ipop = 2)
## Plot with the average path of the treated units and the average of their
## respective synthetic controls:

multi$p