README.md
In tidysynth: A Tidy Implementation of the Synthetic Control Method

tidysynth

tidysynth is a tidy implementation the synthetic control method in R. A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The method aims to model a counterfactual unit using a weighted average of units that did not receive the intervention. The effect of the intervention can be estimated by comparing differences in the observed and synthetic time series. See Abadie et al. 2003, 2010, 2015 for more on the method and use cases.

Building on the Synth package, tidysynth makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.

Specifically, the package:

generates placebo synthetic controls on the fly allowing for inferential statistics;
offers plotting methods to easily explore the fit of the synthetic control and weights;
increases transparency and usability through a pipe-able implementation;
relies on a nested tidy data structure with grab_ prefix functions to easily extract component elements from synthetic control pipeline.

Cran.

install.packages('tidysynth')

Developer Version.

# install.packages("devtools")
devtools::install_github("edunford/tidysynth")

The package uses a pipeline of functions to generate the synthetic control.

| Function | Description | |:-----------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------| | synthetic_control() | Initialize a synth pipeline by specifying the panel series, outcome, and intervention period. This pipeline operates as a nested tbl_df | | generate_predictor() | Create one or more scalar variables summarizing covariate data across a specified time window. These predictor variables are used to fit the synthetic control. | | generate_weights() | Fit the unit and predictor weights used to generate the synthetic control. | | generate_control() | Generate the synthetic control using the optimized weights. |

The following example comes from Abadie et al. 2010, which evaluates the impact of Proposition 99 on cigarette consumption in California.

require(tidysynth)
data("smoking")
smoking %>% dplyr::glimpse()

## Rows: 1,209
## Columns: 7
## $ state     <chr> "Rhode Island", "Tennessee", "Indiana", "Nevada", "Louisiana…
## $ year      <dbl> 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, …
## $ cigsale   <dbl> 123.9, 99.8, 134.6, 189.5, 115.9, 108.4, 265.7, 93.8, 100.3,…
## $ lnincome  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ beer      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ age15to24 <dbl> 0.1831579, 0.1780438, 0.1765159, 0.1615542, 0.1851852, 0.175…
## $ retprice  <dbl> 39.3, 39.9, 30.6, 38.9, 34.3, 38.4, 31.4, 37.3, 36.7, 28.8, …

The method aims to generate a synthetic California using information from a subset of control states (the “donor pool”) where a similar law was not implemented. The donor pool is the subset of case comparisons from which information is borrowed to generate a synthetic version of the treated unit (“California”).

smoking_out <-

  smoking %>%

  # initial the synthetic control object
  synthetic_control(outcome = cigsale, # outcome
                    unit = state, # unit index in the panel data
                    time = year, # time index in the panel data
                    i_unit = "California", # unit where the intervention occurred
                    i_time = 1988, # time period when the intervention occurred
                    generate_placebos=T # generate placebo synthetic controls (for inference)
                    ) %>%

  # Generate the aggregate predictors used to fit the weights

  # average log income, retail price of cigarettes, and proportion of the
  # population between 15 and 24 years of age from 1980 - 1988
  generate_predictor(time_window = 1980:1988,
                     ln_income = mean(lnincome, na.rm = T),
                     ret_price = mean(retprice, na.rm = T),
                     youth = mean(age15to24, na.rm = T)) %>%

  # average beer consumption in the donor pool from 1984 - 1988
  generate_predictor(time_window = 1984:1988,
                     beer_sales = mean(beer, na.rm = T)) %>%

  # Lagged cigarette sales 
  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, # time to use in the optimization task
                   margin_ipop = .02,sigf_ipop = 7,bound_ipop = 6 # optimizer options
  ) %>%

  # Generate the synthetic control
  generate_control()

Once the synthetic control is generated, one can easily assess the fit by comparing the trends of the synthetic and observed time series. The idea is that the trends in the pre-intervention period should map closely onto one another.

smoking_out %>% plot_trends()

To capture the causal quantity (i.e. the difference between the observed and counterfactual), one can plot the differences using plot_differences()

smoking_out %>% plot_differences()

In addition, one can easily examine the weighting of the units and variables in the fit. This allows one to see which cases were used, in part, to generate the synthetic control.

smoking_out %>% plot_weights()

Another useful way of evaluating the synthetic control is to look at how comparable the synthetic control is to the observed covariates of the treated unit.

smoking_out %>% grab_balance_table()

## # A tibble: 7 × 4
##   variable     California synthetic_California donor_sample
##   <chr>             <dbl>                <dbl>        <dbl>
## 1 ln_income        10.1                  9.85         9.83 
## 2 ret_price        89.4                 89.4         87.3  
## 3 youth             0.174                0.174        0.173
## 4 beer_sales       24.3                 24.2         23.7  
## 5 cigsale_1975    127.                 127.         137.   
## 6 cigsale_1980    120.                 120.         138.   
## 7 cigsale_1988     90.1                 91.4        114.

For inference, the method relies on repeating the method for every donor in the donor pool exactly as was done for the treated unit — i.e. generating placebo synthetic controls). By setting generate_placebos = TRUE when initializing the synth pipeline with synthetic_control(), placebo cases are automatically generated when constructing the synthetic control of interest. This makes it easy to explore how unique difference between the observed and synthetic unit is when compared to the placebos.

smoking_out %>% plot_placebos()

Note that the plot_placebos() function automatically prunes any placebos that poorly fit the data in the pre-intervention period. The reason for doing so is purely visual: those units tend to throw off the scale when plotting the placebos. To prune, the function looks at the pre-intervention period mean squared prediction error (MSPE) (i.e. a metric that reflects how well the synthetic control maps to the observed outcome time series in pre-intervention period). If a placebo control has a MSPE that is two times beyond the target case (e.g. “California”), then it’s dropped. To turn off this behavior, set prune = FALSE.

smoking_out %>% plot_placebos(prune = FALSE)

Finally, Adabie et al. 2010 outline a way of constructing Fisher’s Exact P-values by dividing the post-intervention MSPE by the pre-intervention MSPE and then ranking all the cases by this ratio in descending order. A p-value is then constructed by taking the rank/total.[^1] The idea is that if the synthetic control fits the observed time series well (low MSPE in the pre-period) and diverges in the post-period (high MSPE in the post-period) then there is a meaningful effect due to the intervention. If the intervention had no effect, then the post-period and pre-period should continue to map onto one another fairly well, yielding a ratio close to 1. If the placebo units fit the data similarly, then we can’t reject the hull hypothesis that there is no effect brought about by the intervention.

This ratio can be easily plotted using plot_mspe_ratio(), offering insight into the rarity of the case where the intervention actually occurred.

smoking_out %>% plot_mspe_ratio()

For more specific information, there is a significance table that can be extracted with one of the many grab_ prefix functions.

smoking_out %>% grab_significance()

## # A tibble: 39 × 8
##    unit_name      type  pre_mspe post_mspe mspe_ratio  rank fishers_exact_pvalue
##    <chr>          <chr>    <dbl>     <dbl>      <dbl> <int>                <dbl>
##  1 California     Trea…     3.17     392.      124.       1               0.0256
##  2 Georgia        Donor     3.79     179.       47.2      2               0.0513
##  3 Indiana        Donor    25.2      770.       30.6      3               0.0769
##  4 West Virginia  Donor     9.52     284.       29.8      4               0.103 
##  5 Wisconsin      Donor    11.1      268.       24.1      5               0.128 
##  6 Missouri       Donor     3.03      67.8      22.4      6               0.154 
##  7 Texas          Donor    14.4      277.       19.3      7               0.179 
##  8 South Carolina Donor    12.6      234.       18.6      8               0.205 
##  9 Virginia       Donor     9.81      96.4       9.83     9               0.231 
## 10 Nebraska       Donor     6.30      52.9       8.40    10               0.256 
## # ℹ 29 more rows
## # ℹ 1 more variable: z_score <dbl>

In addition to the main data pipeline for generating the synthetic control and the plot_ prefix functions for visualizing the output, there are a number of grab_ prefix functions that offer easy access to the data contained within a synth pipeline object.

At its core, a synth pipeline is simply a nested tibble data frame, where each component of the synthetic control pipeline is accessible.

smoking_out

## # A tibble: 78 × 11
##    .id      .placebo .type .outcome .predictors .synthetic_control .unit_weights
##    <chr>       <dbl> <chr> <list>   <list>      <list>             <list>       
##  1 Califor…        0 trea… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  2 Califor…        0 cont… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  3 Alabama         1 trea… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  4 Alabama         1 cont… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  5 Arkansas        1 trea… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  6 Arkansas        1 cont… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  7 Colorado        1 trea… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  8 Colorado        1 cont… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
##  9 Connect…        1 trea… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
## 10 Connect…        1 cont… <tibble> <tibble>    <tibble [31 × 3]>  <tibble>     
## # ℹ 68 more rows
## # ℹ 4 more variables: .predictor_weights <list>, .original_data <list>,
## #   .meta <list>, .loss <list>

To access the relevant data fields, the grab_ prefix functions come into play.

| Function | Description | |:---------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------| | grab_outcome() | Extract the outcome variable generated by synthetic_control(). | | grab_predictors() | Extract the aggregate-level covariates generated by generate_predictor(). | | grab_unit_weights() | Extract the unit weights generated by generate_weights(). | | grab_predictor_weights() | Extract the predictor variable weights generated by generate_weights(). | | grab_loss() | Extract the RMSE loss of the optimized weights generated by generate_weights(). | | grab_synthetic_control() | Extract the synthetic control generated using generate_control(). | | grab_significance() | Generate inferential statistics comparing the rarity of the unit that actually received the intervention to the placebo units in the donor pool. | | grab_balance_table() | Compare the distributions of the aggregate-level predictors for the observed intervention unit, the synthetic control, and the donor pool average. |

smoking_out %>% grab_synthetic_control()

## # A tibble: 31 × 3
##    time_unit real_y synth_y
##        <dbl>  <dbl>   <dbl>
##  1      1970   123     117.
##  2      1971   121     119.
##  3      1972   124.    124.
##  4      1973   124.    125.
##  5      1974   127.    127.
##  6      1975   127.    127.
##  7      1976   128     128.
##  8      1977   126.    126.
##  9      1978   126.    125.
## 10      1979   122.    123.
## # ℹ 21 more rows

Note that most all the grab_ functions allow for extraction of the placebo units as well.

smoking_out %>% grab_synthetic_control(placebo = T)

## # A tibble: 1,209 × 5
##    .id        .placebo time_unit real_y synth_y
##    <chr>         <dbl>     <dbl>  <dbl>   <dbl>
##  1 California        0      1970   123     117.
##  2 California        0      1971   121     119.
##  3 California        0      1972   124.    124.
##  4 California        0      1973   124.    125.
##  5 California        0      1974   127.    127.
##  6 California        0      1975   127.    127.
##  7 California        0      1976   128     128.
##  8 California        0      1977   126.    126.
##  9 California        0      1978   126.    125.
## 10 California        0      1979   122.    123.
## # ℹ 1,199 more rows

But say I really want to `unnest()`…

In the current implementation, you can unpack an entire synth pipeline using unnest(). The grab_ function is meant to streamline any specific extraction needs. The entire method is built on top of a tidyverse infrastructure, so one can side-step most of the package’s functionality and interact with the synth pipeline output as one would any nested tbl_df object.

smoking_out %>% 
  tidyr::unnest(cols = c(.outcome))

## # A tibble: 1,482 × 50
##    .id        .placebo .type   time_unit California Alabama Arkansas Colorado
##    <chr>         <dbl> <chr>       <dbl>      <dbl>   <dbl>    <dbl>    <dbl>
##  1 California        0 treated      1970       123       NA       NA       NA
##  2 California        0 treated      1971       121       NA       NA       NA
##  3 California        0 treated      1972       124.      NA       NA       NA
##  4 California        0 treated      1973       124.      NA       NA       NA
##  5 California        0 treated      1974       127.      NA       NA       NA
##  6 California        0 treated      1975       127.      NA       NA       NA
##  7 California        0 treated      1976       128       NA       NA       NA
##  8 California        0 treated      1977       126.      NA       NA       NA
##  9 California        0 treated      1978       126.      NA       NA       NA
## 10 California        0 treated      1979       122.      NA       NA       NA
## # ℹ 1,472 more rows
## # ℹ 42 more variables: Connecticut <dbl>, Delaware <dbl>, Georgia <dbl>,
## #   Idaho <dbl>, Illinois <dbl>, Indiana <dbl>, Iowa <dbl>, Kansas <dbl>,
## #   Kentucky <dbl>, Louisiana <dbl>, Maine <dbl>, Minnesota <dbl>,
## #   Mississippi <dbl>, Missouri <dbl>, Montana <dbl>, Nebraska <dbl>,
## #   Nevada <dbl>, `New Hampshire` <dbl>, `New Mexico` <dbl>,
## #   `North Carolina` <dbl>, `North Dakota` <dbl>, Ohio <dbl>, Oklahoma <dbl>, …

Spot an issue? Please let me know by posting an issue.

[^1]: Note this implies that you’d need at least 20 cases in the donor pool to get a conventional p-value (.05).