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Title:

Plausible Bounds for Treatment Path Estimates

Version:

1.0.1

Description:

Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) https://simonfreyaldenhoven.github.io/papers/Plausible_bounds.pdf. Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands.

License:

MIT + file LICENSE

Depends:

R (≥ 3.5)

Imports:

dplyr, ggplot2, Matrix, MASS, magrittr, cli

Suggests:

knitr, rmarkdown, doParallel, foreach, testthat (≥ 3.0.0)

Config/testthat/edition:

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

URL:

https://github.com/SimonFreyaldenhoven/plausibounds

BugReports:

https://github.com/SimonFreyaldenhoven/plausibounds/issues

VignetteBuilder:

knitr

Config/Needs/website:

quarto, rmarkdown

NeedsCompilation:

Packaged:

2026-01-14 17:19:36 UTC; C1RAK01

Author:

Simon Freyaldenhoven [aut, cph], Christian Hansen [aut, cph], Ryan Kobler [aut, cre, cph]

Maintainer:

Ryan Kobler <kobleary@gmail.com>

Repository:

CRAN

Date/Publication:

2026-01-20 10:30:02 UTC

plausibounds: Plausible Bounds for Treatment Path Estimates

Description

Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026). Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands.

Main Functions

plausible_bounds: Calculate plausible bounds
create_plot: Create plots of the bounds

Example Datasets

The package includes example datasets to demonstrate the functionality:

estimates_constant and var_constant: A simple case with constant estimates and no correlation
estimates_bighump and var_bighump: A case with sinusoidal estimates and moderate correlation
estimates_smooth and var_smooth: A smooth case with effects that slowly level off and no correlation, from Figure 1 of Freyaldenhoven and Hansen (2026)

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Create Plot for Plausible Bounds

Description

This function creates a plot of plausible bounds from a plausible_bounds object. The plot displays plausible bounds as the main visualization, with optional pointwise and sup-t bounds overlays. Supports event study designs with pre-treatment periods.

Usage

create_plot(
  result,
  show_supt = TRUE,
  show_pointwise = TRUE,
  show_annotations = TRUE
)

Arguments

result

A plausible_bounds object returned by the plausible_bounds() function

show_supt

Whether to show sup-t bounds (default: TRUE)

show_pointwise

Whether to show pointwise bounds (default: TRUE)

show_annotations

Whether to show annotations with test statistics and ATE (default: TRUE)

Value

A ggplot2 object

Examples


# Example with bighump estimates and correlation between estimates
data(estimates_bighump)
data(var_bighump)
result_complex <- plausible_bounds(estimates_bighump[1:4], var_bighump[1:4, 1:4])
plot_complex <- create_plot(result_complex)

Sinusoidal Estimates with Moderate Correlation

Description

A dataset containing estimates with a curved sinusoidal pattern in the first 6 periods that then converges to zero for the remaining 30 periods. The effect is a smooth curved trajectory with a large dip before the treatment effect quickly returns to 0. Generated with moderate correlation (rho = 0.5).

Usage

estimates_bighump

Format

A numeric vector with 36 elements

Source

Generated from simulation with sinusoidal design and moderate correlation (rho = 0.5)

Examples

data(estimates_bighump)
data(var_bighump)
result <- plausible_bounds(estimates_bighump[1:4], var_bighump[1:4, 1:4])
create_plot(result)

Constant Estimates

Description

A dataset containing estimates from a simple constant design, no correlation across horizons.

Usage

estimates_constant

Format

A numeric vector with 12 elements

Source

Generated from simulation with constant design and no correlation across horizons

Examples

data(estimates_constant)
data(var_constant)
result <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
create_plot(result)

Smooth Estimates from Freyaldenhoven and Hansen (2026)

Description

A dataset containing smooth treatment effect estimates that dip down and then converge to zero. The first 8 observations comprise the preperiods, the next 36 are post-period.

Usage

estimates_smooth

Format

A numeric vector with 44 elements (8 preperiods, 36 postperiods)

Source

Point estimates from Figure 1 of Freyaldenhoven and Hansen (2026)

Examples

data(estimates_smooth)
data(var_smooth)
result <- plausible_bounds(estimates_smooth[9:13], var_smooth[9:13, 9:13])
create_plot(result)

Calculate Plausible Bounds

Description

This function calculates the plausible bounds for a vector of estimates along with average treatment effect, Wald tests, and optional pointwise/sup-t bounds. Supports pre-treatment periods for event study designs.

Usage

plausible_bounds(
  estimates,
  var,
  alpha = 0.05,
  preperiods = 0,
  include_pointwise = TRUE,
  include_supt = TRUE,
  parallel = FALSE,
  n_cores = NULL
)

Arguments

estimates

A numeric vector or single-row/single-column matrix of point estimates. If preperiods > 0, the first preperiods elements are pre-treatment estimates, followed by post-treatment estimates.

var

The variance-covariance matrix of the estimates

alpha

Significance level (default: 0.05)

preperiods

Number of pre-treatment periods (default: 0). Period 0 is assumed to be normalized and not included in estimates.

include_pointwise

Whether to include pointwise bounds (default: TRUE)

include_supt

Whether to include sup-t bounds (default: TRUE)

parallel

Whether to use parallel processing for restricted bounds calculation (default: FALSE)

n_cores

Number of cores to use for parallel processing (default: NULL, which uses detectCores() - 1). Only used when parallel = TRUE.

Value

A list containing:

alpha

Significance level

preperiods

Number of pre-treatment periods

wald_test

List with post (and pre if preperiods > 0) Wald test results

restricted_bounds

Data frame with horizon, unrestr_est, restr_est, lower, upper

restricted_bounds_metadata

List with supt_critval, supt_b, degrees_of_freedom, K, lambda1, lambda2, restr_class, best_fit_model

avg_treatment_effect

List with estimate, se, lower, upper

pointwise_bounds

List with lower and upper vectors (if include_pointwise = TRUE)

supt_bounds

List with lower and upper vectors (if include_supt = TRUE)

Examples

# Example with constant estimates and no correlation (simple case)
data(estimates_constant)
data(var_constant)
pb <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
print(pb)
summary(pb)

Print method for plausible_bounds objects

Description

Print method for plausible_bounds objects

Usage

## S3 method for class 'plausible_bounds'
print(x, ...)

Arguments

x

A plausible_bounds object

...

Additional arguments passed to print

Value

Invisibly returns a plausible_bounds object and displays summary of main results

Examples

# Example with constant estimates and no correlation (simple case)
data(estimates_constant)
data(var_constant)
pb <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
print(pb)

Print method for summary.plausible_bounds objects

Description

Print method for summary.plausible_bounds objects

Usage

## S3 method for class 'summary.plausible_bounds'
print(x, ...)

Arguments

x

A summary.plausible_bounds object

...

Additional arguments passed to print

Value

Invisibly returns the input summary.plausible_bounds object and displays summary data.frame of restricted estimates and plausible bounds.

Examples

# Example with constant design and no correlation
data(estimates_constant)
data(var_constant)
pb <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
pb_df <- summary(pb)
print(pb_df)

Summary method for plausible_bounds objects

Description

Summary method for plausible_bounds objects

Usage

## S3 method for class 'plausible_bounds'
summary(object, ...)

Arguments

object

A plausible_bounds object

...

Additional arguments passed to summary

Value

A data.frame containing the horizon, unrestricted estimates, restricted estimates, and plausible bounds

Examples

# Example with constant estimates and no correlation (simple case)
data(estimates_constant)
data(var_constant)
pb <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
print(pb)
summary(pb)

Variance Matrix for Sinusoidal Estimates

Description

A variance matrix for the sinusoidal estimates with moderate correlation structure (rho = 0.5). This matrix has non-zero off-diagonal elements representing moderate correlation across time periods.

Usage

var_bighump

Format

A 36 x 36 matrix

Source

Generated from simulation with sinusoidal design and moderate correlation (rho = 0.5)

Examples

data(estimates_bighump)
data(var_bighump)
result <- plausible_bounds(estimates_bighump[1:4], var_bighump[1:4, 1:4])
create_plot(result)

Variance Matrix for Constant Estimates

Description

A variance matrix for the constant estimates. This is a diagonal matrix representing no correlation across time periods.

Usage

var_constant

Format

A 12 x 12 matrix

Source

Generated from simulation with constant design and no correlation across horizons.

Examples

data(estimates_constant)
data(var_constant)
result <- plausible_bounds(estimates_constant[1:4], var_constant[1:4, 1:4])
create_plot(result)

Variance Matrix for Smooth Estimates

Description

A variance matrix for the smooth estimates. This matrix captures the correlation structure of the estimates. The first 8 rows and 8 columns comprise the variance matrix for the 8 preperiods.

Usage

var_smooth

Format

A 44 x 44 matrix

Source

Variance Matrix from Figure 1 of Freyaldenhoven and Hansen (2026)

Examples

data(estimates_smooth)
data(var_smooth)
result <- plausible_bounds(estimates_smooth[9:13], var_smooth[9:13, 9:13])
create_plot(result)