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In coresynth: Fast and Unified Synthetic Control Methods

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coresynth provides six causal-inference estimators for panel data behind a single formula interface, with the computational core (QP solving, SVD, Kalman filtering) written in C++ via RcppArmadillo. This vignette walks through the basics: fitting a model, comparing methods, and pulling results out with broom and plot().

library(coresynth)

The unified formula

Every estimator is reached through scm_fit() with the same formula syntax:

outcome ~ treatment | unit_id + time_id

The data must be a long-format balanced panel (one row per unit–time), and treatment is a 0/1 indicator that switches on for treated units in post-treatment periods. method = selects the estimator.

A toy panel

We simulate a balanced panel of 10 units over 20 periods. Unit u1 is treated from period 11 onward with a true ATT of 2.0.

set.seed(42)
N <- 10; TT <- 20; T_pre <- 10
f   <- cumsum(rnorm(TT, 0, 0.5))     # common factor
lam <- rnorm(N, 1, 0.3)              # unit loadings
dat <- expand.grid(time = seq_len(TT), id = paste0("u", seq_len(N)))
dat$y <- as.vector(outer(f, lam)) + rnorm(nrow(dat), 0, 0.3)
dat$d <- as.integer(dat$id == "u1" & dat$time > T_pre)
dat$y[dat$d == 1] <- dat$y[dat$d == 1] + 2.0   # inject the treatment effect

head(dat)

Fitting one method

fit <- scm_fit(y ~ d | id + time, data = dat, method = "scm")
fit

The estimated ATT lives in fit$estimate:

fit$estimate

Comparing all six methods

Because the interface is shared, swapping estimators is a one-word change. Here we run all six on the same data (true ATT = 2.0).

methods <- c("scm", "sdid", "gsc", "mc", "tasc", "si")
fits    <- lapply(methods, function(m) scm_fit(y ~ d | id + time, data = dat, method = m))
names(fits) <- methods

data.frame(
  method   = methods,
  estimate = round(sapply(fits, `[[`, "estimate"), 3)
)

| Method | Full name | Reference | |----|----|----| | scm | Synthetic Control Method | Abadie, Diamond & Hainmueller (2010) | | sdid | Synthetic Difference-in-Differences | Arkhangelsky et al. (2021) | | gsc | Generalized Synthetic Control | Xu (2017) | | mc | Matrix Completion | Athey et al. (2021) | | tasc | Time-Aware Synthetic Control | Rho et al. (2026) | | si | Synthetic Interventions | Agarwal et al. (2025) |

Visualizing a fit

plot.coresynth() offers three views via type =.

plot(fits$sdid, type = "trend")   # observed vs. synthetic

plot(fits$scm, type = "gap")      # treatment effect over time

plot(fits$scm, type = "weights")  # donor weights

tidy / glance / augment

coresynth integrates with broom, so results drop straight into tidy workflows and paper tables.

library(broom)

tidy(fits$scm)     # donor weights as a data frame
glance(fits$scm)   # one-row model summary

For custom plots or diagnostics, the accessor generics return the underlying series from any fit under a uniform interface, whatever the estimation method:

head(treated_outcomes(fits$scm))    # observed treated series
head(synthetic_outcomes(fits$scm))  # estimated counterfactual
dim(donor_outcomes(fits$scm))       # T x N_co donor outcome matrix

export_json() writes a fit to disk as JSON for reproducibility or downstream (e.g. AI) workflows:

export_json(fits$scm, file = "scm_result.json")

Where to next

Estimators — covariates, predictors, and method-specific options for all six estimators plus the experimental-design variant.
Inference — placebo, bootstrap, jackknife, parametric, and conformal inference.
Staggered adoption — cohort-based estimation when units adopt treatment at different times.