generate_control: generate_control

In tidysynth: A Tidy Implementation of the Synthetic Control Method

generate_control

Description

Uses the weights generated from generate_weights() to weight control units from the donor pool to denerate a synthetic version of the treated unit time series.

Usage

generate_control(data)

Arguments

data	nested data of type tbl_df generated from sythetic_control(). See synthetic_control() documentation for more information. In addition, .unit_weights must be generate using generate_weights(). See documentation for more information on how to generate weights.

Value

tbl_df with nested fields containing the following:

• .id: unit id for the intervention case (this will differ when a placebo unit).

• .placebo: indicator field taking on the value of 1 if a unit is a placebo unit, 0 if it's the specified treated unit.

• .type: type of the nested data construct: treated or controls. Keeps tract of which data construct is located in .outcome field.

• .outcome: nested data construct containing the outcome variable configured for the sythnetic control method. Data is configured into a wide format for the optimization task.

• .predictors: nested data construct containing the covariate matrices for the treated and control (donor) units. Data is configured into a wide format for the optimization task.

• .synthetic_control: nested data construct containing the synthetic control version of the outcome variable generated from the unit weights.

• .unit_weights: Nested column of unit weights (i.e. how each unit from the donor pool contributes to the synthetic control). Weights should sum to

• .predictor_weights: Nested column of predictor variable weights (i.e. the significance of each predictor in optimizing the weights that generate the synthetic control). Weights should sum to 1. If variable weights are provided, those variable weights are provided.

• .original_data: original impute data filtered by treated or control units. This allows for easy processing down stream when generating predictors.

• .meta: stores information regarding the unit and time index, the treated unit and time and the name of the outcome variable. Used downstream in subsequent functions.

• .loss: the RMPE loss for both sets of weights.

Examples

# Smoking example data
data(smoking)

smoking_out <-
smoking %>%

# initial the synthetic control object
synthetic_control(outcome = cigsale,
                  unit = state,
                  time = year,
                  i_unit = "California",
                  i_time = 1988,
                  generate_placebos= FALSE) %>%

# Generate the aggregate predictors used to generate the weights
  generate_predictor(time_window=1980:1988,
                     lnincome = mean(lnincome, na.rm = TRUE),
                     retprice = mean(retprice, na.rm = TRUE),
                     age15to24 = mean(age15to24, na.rm = TRUE)) %>%

  generate_predictor(time_window=1984:1988,
                     beer = mean(beer, na.rm = TRUE)) %>%

  generate_predictor(time_window=1975,
                     cigsale_1975 = cigsale) %>%

  generate_predictor(time_window=1980,
                     cigsale_1980 = cigsale) %>%

  generate_predictor(time_window=1988,
                     cigsale_1988 = cigsale) %>%


  # Generate the fitted weights for the synthetic control
  generate_weights(optimization_window =1970:1988,
                   Margin.ipop=.02,Sigf.ipop=7,Bound.ipop=6) %>%

  # Generate the synthetic control
  generate_control()

# Plot the observed and synthetic trend
smoking_out %>% plot_trends(time_window = 1970:2000)

tidysynth documentation built on May 31, 2023, 6:13 p.m.