Get started with coresynth

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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().

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)
#>   time id         y d
#> 1    1 u1 0.7590559 0
#> 2    2 u1 0.5774968 0
#> 3    3 u1 0.8414384 0
#> 4    4 u1 0.6355518 0
#> 5    5 u1 1.1532557 0
#> 6    6 u1 0.4384856 0

Fitting one method

fit <- scm_fit(y ~ d | id + time, data = dat, method = "scm")
fit
#> === coresynth fit ===
#> Method : SCM 
#> Estimate (ATT): 2.2711 
#> Pre-treatment periods: 10

The estimated ATT lives in fit$estimate:

fit$estimate
#> [1] 2.271125

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 estimate
#> scm     scm    2.271
#> sdid   sdid    2.150
#> gsc     gsc    2.255
#> mc       mc    2.696
#> tasc   tasc    1.154
#> si       si    2.346

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$scm)     # donor weights as a data frame
#>   term   estimate        type
#> 1   u2 0.24752069 unit_weight
#> 2   u3 0.00000000 unit_weight
#> 3   u4 0.07055224 unit_weight
#> 4   u5 0.00000000 unit_weight
#> 5   u6 0.14767482 unit_weight
#> 6   u7 0.32770301 unit_weight
#> 7   u8 0.00000000 unit_weight
#> 8   u9 0.00000000 unit_weight
#> 9  u10 0.20654924 unit_weight
glance(fits$scm)   # one-row model summary
#>   method estimate n_controls n_treated T_pre T_post staggered multi_arm
#> 1    scm 2.271125          9         1    10     10     FALSE     FALSE

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$scm))    # observed treated series
#> [1] 0.7590559 0.5774968 0.8414384 0.6355518 1.1532557 0.4384856
head(synthetic_outcomes(fits$scm))  # estimated counterfactual
#> [1] 0.5549202 0.2098675 0.7254600 0.8242011 0.9246564 0.6843428
dim(donor_outcomes(fits$scm))       # T x N_co donor outcome matrix
#> [1] 20  9

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.