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coresynth is a high-performance R package that provides six causal inference methods for panel data through a unified formula interface. All core optimizations (QP solving, SVD, Kalman filtering) are implemented in C++ via RcppArmadillo, so estimation stays fast even on larger donor pools (see the Performance section for timings).
# From GitHub (requires Rtools on Windows, Xcode on macOS)
pak::pak("yo5uke/coresynth")
# Via devtools
devtools::install_github("yo5uke/coresynth")library(coresynth)
# Generate a balanced panel (10 units, 20 periods, true ATT = 2.0)
set.seed(42)
N <- 10; TT <- 20; T_pre <- 10
f <- cumsum(rnorm(TT, 0, 0.5))
lam <- rnorm(N, 1, 0.3)
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 # true ATT = 2
# Run all six methods
methods <- c("scm", "sdid", "gsc", "mc", "tasc", "si")
fits <- lapply(methods, \(m) scm_fit(y ~ d | id + time, data = dat, method = m))
names(fits) <- methods
# Compare ATT estimates (true value = 2.0)
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# SDID trend plot (observed vs. synthetic)
plot(fits$sdid, type = "trend")
# SCM gap plot (treatment effect over time)
plot(fits$scm, type = "gap")
# SCM donor weights
plot(fits$scm, type = "weights")
| Method | Reference | Treatment | Covariates | Inference |
|---|---|---|---|---|
scm |
Abadie, Diamond & Hainmueller (2010) | Sharp & Staggered | pred() list |
mspe_ratio_pval(),
conformal_inference() |
sdid |
Arkhangelsky et al. (2021) | Sharp & Staggered | covariates= |
sdid_inference(),
conformal_inference() |
gsc |
Xu (2017) | Sharp & Staggered | covariates=
time-varying |
gsc_boot(), gsc_inference(),
conformal_inference() |
mc |
Athey et al. (2021) | Sharp & Staggered | — | conformal_inference() |
tasc |
Rho et al. (2026) | Sharp & Staggered | — | — |
si |
Agarwal et al. (2025) | Sharp, Staggered & Multi-arm | — | si_inference(), conformal_inference() |
conformal_inference() (Chernozhukov, Wüthrich & Zhu
2021) provides permutation-based p-values and confidence intervals for
sharp fits across
scm/sdid/gsc/mc/si.
All six methods support staggered adoption using a cohort-based
approach (Clarke et al. 2023): each adoption cohort is fitted separately
and the cohort ATTs are aggregated with weights proportional to
N_treated × T_post.
# u1: treated from t=11, u2: treated from t=16
dat_s <- dat
dat_s$d <- 0L
dat_s$d[dat_s$id == "u1" & dat_s$time > 10] <- 1L
dat_s$d[dat_s$id == "u2" & dat_s$time > 15] <- 1L
dat_s$y[dat_s$d == 1] <- dat_s$y[dat_s$d == 1] + 2.0
# All methods detect staggered timing automatically
fit_sdid <- scm_fit(y ~ d | id + time, data = dat_s, method = "sdid")
fit_gsc <- scm_fit(y ~ d | id + time, data = dat_s, method = "gsc")
fit_mc <- scm_fit(y ~ d | id + time, data = dat_s, method = "mc")
fit_si <- scm_fit(y ~ d | id + time, data = dat_s, method = "si")
# Cohort-level estimates are accessible
fit_sdid$cohort_estimates
#> cohort estimate weight n_treated T_pre T_post
#> 1 11 1.97 0.667 1 10 9
#> 2 16 2.03 0.333 1 10 4
# control_group = "clean" (default) uses never-treated + future-adopters as donors
# control_group = "never_treated" restricts to never-treated only
fit_sdid_clean <- scm_fit(y ~ d | id + time, data = dat_s, method = "sdid",
control_group = "never_treated")pred()SCM supports covariate-based matching following Abadie et al. (2010)
§2.3. Use pred(vars, times, op) to specify which variables
and time windows to include in the predictor matrix:
# Assume dat has extra columns: income, unemp
fit_scm_cov <- scm_fit(
y ~ d | id + time,
data = dat,
method = "scm",
predictors = list(
pred(c("income", "unemp"), 1:8), # average income & unemp over pre-period
pred("y", 5), # outcome at a specific pre-treatment year
pred("y", 1:4, op = "mean") # outcome averaged over early pre-period
)
)
summary(fit_scm_cov) # shows predictor balance tableEach pred() call aggregates one or more variables over a
time window using op = "mean" (default),
"median", or "sum". Multiple
pred() calls with different windows can be combined freely
in the list.
GSC supports time-varying covariate adjustment via the full EM
algorithm of Xu (2017). Pass a character vector of column names as
covariates:
# Assume dat has a time-varying column: gdp_growth
fit_gsc_cov <- scm_fit(
y ~ d | id + time,
data = dat,
method = "gsc",
r = 2,
covariates = "gdp_growth"
)
fit_gsc_cov$beta # estimated beta coefficient(s)The EM loop alternates between:
Treated unit loadings are estimated from covariate-demeaned
pre-treatment data per Xu (2017) Step 2. When
covariates = NULL (default), the plain 3-step SVD estimator
(\(\hat\beta = 0\)) is used.
# SCM: MSPE ratio placebo test (Abadie et al. 2010)
scm_p <- mspe_ratio_pval(fits$scm)
cat("p-value:", scm_p$p_value, "\n")
# SDID: four inference methods — placebo, bootstrap, jackknife, jackknife_global
sdid_inf <- sdid_inference(fits$sdid, method = "placebo")
tidy(sdid_inf) # broom-style one-row data.frame
sdid_boot <- sdid_inference(fits$sdid, method = "bootstrap", n_boot = 200, seed = 1)
tidy(sdid_boot)
# GSC: parametric bootstrap under H0 (sharp only)
gsc_ci <- gsc_boot(fits$gsc, B = 200, alpha = 0.05)
cat("95% CI: [", gsc_ci$ci_lower, ",", gsc_ci$ci_upper, "]\n")
# GSC / SI: non-parametric inference (sharp + staggered)
gsc_inf <- gsc_inference(fits$gsc, method = "jackknife")
si_inf <- si_inference(fits$si, method = "bootstrap", n_boot = 200, seed = 1)
tidy(gsc_inf)
tidy(si_inf)
# Conformal inference (Chernozhukov, Wüthrich & Zhu 2021) — sharp fits for
# scm / sdid / gsc / mc / si. Re-estimates the counterfactual under the null
# and inverts a moving-block permutation test for a p-value and CI.
conf <- conformal_inference(fits$scm, tau0 = 0, level = 0.95)
tidy(conf)library(broom)
# Extract weights as a data frame
tidy(fits$scm)
# Summary row
glance(fits$scm)
# JSON export (for reproducibility and AI workflows)
export_json(fits$scm, file = "scm_result.json")Estimation stays fast even as the donor pool grows. SCM fit times (Windows 11 / R 4.6.0 / GCC 14.2.0):
| N_co | T_pre | coresynth |
|---|---|---|
| 16 | 10 | 105 ms |
| 20 | 20 | 72 ms |
| 50 | 30 | 916 ms |
| 100 | 40 | 6,300 ms |
A full SCM fit on a 100-donor pool completes in a few seconds.
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.