Package {rdstagger}

rdstagger: Staggered Regression Discontinuity with Network Interference

Description

Implements a unified framework combining staggered difference-in-differences with regression discontinuity designs and network interference. Extends Callaway and Sant'Anna (2021) to settings where:

1. Treatment assignment is determined by a running variable crossing a cutoff (RD)

2. Treatment adoption timing is heterogeneous across units (staggered DiD)

3. Spillover effects operate through a known network structure (interference)

Main functions

sim_rdstagger

Simulate a staggered RD panel dataset with interference

rdstagger_bw

Optimal bandwidth selection per cohort-time cell

rdstagger_attgt

Estimate ATT(g,t) — direct and spillover effects

rdstagger_spillover

Estimate spillover effects at network distance d

rdstagger_agg

Aggregate ATT(g,t) into event-study or overall ATT

rdstagger_pretest

Pre-treatment parallel trends falsification tests

References

Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.

Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica, 82(6), 2295-2326.

Manski, C. F. (2013). Identification of treatment response with social interactions. The Econometrics Journal, 16(1), S1-S23.

Author(s)

Maintainer: Subir Hait haitsubi@msu.edu

See Also

Useful links:

• https://github.com/causalfragility-lab/rdstagger

• Report bugs at https://github.com/causalfragility-lab/rdstagger/issues

Plot Aggregated ATT Estimates

Description

Produces a ggplot2 event-study or aggregation plot from an "rdstagger_agg" object.

Usage

## S3 method for class 'rdstagger_agg'
plot(x, ...)

Arguments

Value

A ggplot2 object.

Examples

sim <- sim_rdstagger(n = 300, nperiods = 6, n_cohorts = 2,
                     true_direct = 0.3, seed = 42)
res <- rdstagger_attgt(data = sim$data, yname = "y", xname = "x",
                       gname = "g", tname = "period", idname = "id",
                       bw = 1.5, boot = FALSE)
agg <- rdstagger_agg(res, type = "dynamic")
plot(agg)

Aggregate ATT(g,t) Estimates

Description

Aggregates group-time average treatment effects ATT(g,t) from rdstagger_attgt into summary estimands: event-study (dynamic), cohort-level, calendar-time, or overall ATT.

Usage

rdstagger_agg(
  x,
  type = c("dynamic", "group", "calendar", "overall"),
  min_periods = 1L
)

Arguments

Value

An object of class "rdstagger_agg", a list with:

agg

Data frame of aggregated estimates

type

Aggregation type used

overall_att

Simple overall ATT (post-treatment average)

attgt

The original ATT(g,t) data frame

Examples

sim <- sim_rdstagger(n = 300, nperiods = 6, n_cohorts = 2,
                     true_direct = 0.3, seed = 42)
res <- rdstagger_attgt(data = sim$data, yname = "y", xname = "x",
                       gname = "g", tname = "period", idname = "id",
                       bw = 1.5, boot = FALSE)

# Event study
agg_dyn <- rdstagger_agg(res, type = "dynamic")
print(agg_dyn)
plot(agg_dyn)

# Overall ATT
agg_ov <- rdstagger_agg(res, type = "overall")
print(agg_ov)

Estimate Group-Time Average Treatment Effects in Staggered RD

Description

Main estimation function for the staggered RD framework with network interference. Estimates ATT(g, t) — the average treatment effect for cohort g at time t — separately for direct effects on treated units and spillover effects on their network neighbors, within an RD bandwidth around the cutoff.

Usage

rdstagger_attgt(
  data,
  yname,
  xname,
  cutoff = 0,
  gname,
  tname,
  idname,
  network = NULL,
  bw = "optimal",
  control_group = c("nevertreated", "notyetreated"),
  xformla = NULL,
  doubly_robust = TRUE,
  boot = TRUE,
  nboot = 999L,
  alpha = 0.05,
  kernel = c("triangular", "epanechnikov", "uniform")
)

Arguments

Value

An object of class "rdstagger_attgt", a list with:

attgt

Data frame of ATT(g,t) estimates (direct effects)

spillgt

Data frame of spillover ATT(g,t) estimates (if network supplied)

args

List of call arguments

bandwidth

Bandwidth used

Examples

sim <- sim_rdstagger(n = 300, nperiods = 6, n_cohorts = 2,
                     true_direct = 0.3, true_spill = 0.1, seed = 42)

res <- rdstagger_attgt(
  data    = sim$data,
  yname   = "y",
  xname   = "x",
  cutoff  = 0,
  gname   = "g",
  tname   = "period",
  idname  = "id",
  network = sim$network,
  bw      = 1.5,
  boot    = FALSE
)
print(res)

Bandwidth Selection for Staggered RD

Description

Computes an optimal bandwidth for each cohort-time cell using the mean-squared-error-optimal bandwidth selector from rdrobust. Separate bandwidths are estimated for pre-treatment and post-treatment periods to ensure appropriate comparison groups.

Usage

rdstagger_bw(
  data,
  yname,
  xname,
  cutoff = 0,
  gname,
  tname,
  kernel = c("triangular", "epanechnikov", "uniform"),
  bw_common = FALSE
)

Arguments

Value

A list with elements:

bw_matrix

A matrix of bandwidths with rows = cohorts, columns = time periods

bw_common

Single common bandwidth (median across cells)

bw_summary

A data.frame summarising bandwidths by cohort and period

Examples

sim <- sim_rdstagger(n = 400, nperiods = 6, n_cohorts = 2, seed = 42)
bw  <- rdstagger_bw(data = sim$data, yname = "y", xname = "x",
                    cutoff = 0, gname = "g", tname = "period")
bw$bw_common
bw$bw_summary

Pre-Treatment Falsification Tests for Staggered RD

Description

Tests the pre-treatment parallel trends assumption within the RD bandwidth. Performs a joint test across all pre-treatment cohort-time cells and individual cell tests, analogous to pretest in the did package but adapted for the staggered RD setting.

Usage

rdstagger_pretest(x, method = c("joint", "individual", "both"))

Arguments

Value

A list with elements:

joint

Joint test statistic, df, and p-value (if requested)

individual

Data frame of per-cell tests (if requested)

passes

Logical. TRUE if joint test p-value > 0.05

Examples

sim <- sim_rdstagger(n = 400, nperiods = 8, n_cohorts = 2,
                     true_direct = 0.3, seed = 42)
res <- rdstagger_attgt(data = sim$data, yname = "y", xname = "x",
                       gname = "g", tname = "period", idname = "id",
                       bw = 1.5, boot = FALSE)
pt  <- rdstagger_pretest(res)
print(pt)

Estimate Spillover Effects in Staggered RD

Description

Estimates the spillover (indirect) treatment effects on network neighbors of treated units within the RD bandwidth. Spillover effects are estimated separately for each cohort-time cell.

Usage

rdstagger_spillover(
  data,
  yname,
  xname,
  cutoff = 0,
  gname,
  tname,
  idname,
  network,
  bw,
  kernel = c("triangular", "epanechnikov", "uniform"),
  boot = TRUE,
  nboot = 999L,
  alpha = 0.05
)

Arguments

Value

A data.frame with columns:

cohort

Treatment cohort

period

Time period

spill_att

Spillover ATT estimate

se

Standard error

ci_lower, ci_upper

Confidence interval

pval

p-value

n_exposed

Number of exposed neighbors

Examples

sim <- sim_rdstagger(n = 300, nperiods = 6, n_cohorts = 2,
                     true_direct = 0.3, true_spill = 0.15, seed = 42)
sp  <- rdstagger_spillover(
  data    = sim$data,  yname = "y",   xname = "x",
  gname   = "g",       tname = "period", idname = "id",
  network = sim$network, bw = 1.5, boot = FALSE
)
head(sp)

Simulate a Staggered RD Panel Dataset with Network Interference

Description

Generates synthetic panel data suitable for testing and demonstrating the rdstagger estimators. The data generating process features a running variable with a cutoff-based treatment assignment, staggered adoption across cohorts, and network spillover effects.

Usage

sim_rdstagger(
  n = 500,
  nperiods = 8,
  n_cohorts = 3,
  cutoff = 0,
  bw = 1,
  network_density = 0.1,
  true_direct = 0.3,
  true_spill = 0.1,
  outcome_type = c("continuous", "binary", "count"),
  heterogeneous_te = FALSE,
  seed = NULL
)

Arguments

Value

A list with three elements:

data

A data.frame with columns: id, period, y, x (running variable), g (cohort, Inf for never-treated), treated, neighbor_treated, spillover_share

network

An n \times n adjacency matrix

true_params

A list of the true parameter values used to generate the data

Examples

# Basic continuous outcome
sim <- sim_rdstagger(n = 300, nperiods = 6, n_cohorts = 2,
                     true_direct = 0.3, true_spill = 0.1, seed = 42)
head(sim$data)
sim$true_params

# Binary outcome
sim_bin <- sim_rdstagger(n = 500, nperiods = 8, n_cohorts = 3,
                         outcome_type = "binary", seed = 123)
table(sim_bin$data$y)

# Count outcome
sim_cnt <- sim_rdstagger(n = 400, nperiods = 6, n_cohorts = 2,
                         outcome_type = "count", true_direct = 0.5,
                         seed = 999)