The All-in-One Causal Inference Toolkit • causalverse

causalverse is the all-in-one R toolkit for causal inference — the tidyverse of causal analysis. Version 1.1.0 provides 80+ utility functions, 13 in-depth vignettes, and seamless integration with 100+ backend packages, covering the full pipeline from experimental design through publication-ready reporting.

The package covers 13 major method families:

Method	Key Functions	Backend Packages
Synthetic DID	`synthdid_est()`, `synthdid_est_ate()`, `sc_gap_plot()`	synthdid
Difference-in-Differences	`did_event_study()`, `staggered_summary()`, `bacon_decomp_plot()`, `did_power_analysis()`	fixest, did, did2s, bacondecomp, HonestDiD
Randomized Experiments	`mde_calc()`, `dr_ate()`, `placebo_test()`, `attrition_analysis()`, `power_sim()`	estimatr, grf, DoubleML
Regression Discontinuity	`rd_plot()`, `rd_assumption_tests()`, `rd_bandwidth_sensitivity()`	rdrobust, rddensity, rdpower
Synthetic Controls	`panel_estimate()`, `sc_gap_plot()`	Synth, augsynth, gsynth
Instrumental Variables	`iv_diagnostic_summary()`, `iv_sensitivity()`	ivreg, ivmodel, hdm, MendelianRandomization
Finance Event Studies	`event_study_finance()`, `plot_event_coefs()`	fixest, estudy2
Matching & Weighting	`balance_plot()`, `love_plot()`, `overlap_weights()`, `covariate_summary()`	MatchIt, cobalt, WeightIt
Heterogeneous Effects	`blp_analysis()`, `qini_curve()`, `causal_forest_summary()`, `heterogeneity_plot()`	grf, policytree
Mediation Analysis	`mediation_analysis()`, `lee_bounds()`	mediation, lavaan
Panel Data	`panel_diagnostics()`	plm, tseries
Causal DAGs	`dag_plot()`, `dag_adjustment_sets()`, `dag_test_implications()`	dagitty, ggdag
Sensitivity & Robustness	`multiverse_analysis()`, `spec_curve()`, `sensitivity_plot()`, `pretrend_sensitivity()`	sensemakr, EValue, HonestDiD

How to cite this package

You can cite this package as follows: “we utilized the causal inference methodologies from the causalverse R package (Nguyen 2026)”. Here is the full bibliographic reference to include in your reference list (don’t forget to update the ‘last accessed’ date):

Nguyen, M. (2026). causalverse: The All-in-One Causal Inference Toolkit (Version 1.1.0). Zenodo. https://doi.org/10.5281/zenodo.8254063. Retrieved from https://github.com/mikenguyen13/causalverse.

Vignettes

13 comprehensive, journal-quality tutorials covering every major causal inference method:

0. Introduction – Package overview, function index, quick-start examples
1. Randomized Control Trials – RCTs, adaptive experiments, SUTVA, interference
2. Regression Discontinuity – Sharp, fuzzy, multi-cutoff, assumption tests
3. Synthetic DID – SDID, staggered SDID, placebo inference
4. Difference-in-Differences – TWFE, CS DiD, stacked DiD, HonestDiD
5. Synthetic Controls – SCM, augmented SC, penalized SC, RMSPE inference
6. Instrumental Variables – 2SLS, Bartik, Mendelian Randomization, MTE
7. Finance Event Studies – CAR, BHAR, wealth effects, intraday
8. Matching Methods – PSM, CEM, genetic matching, entropy balancing
9. Sensitivity Analysis – OVB, E-values, Manski bounds, spec curves
10. Heterogeneous Treatment Effects – Causal forests, BLP, GATES, Qini, DML
11. Causal Mediation – ACME, ADE, SEM, moderated mediation
12. Panel Data Methods – FE, RE, GMM, unit roots, cross-sectional dependence

Installation

You can install the development version of causalverse from GitHub with:

# install.packages("pak")
pak::pkg_install("mikenguyen13/causalverse")

Or using devtools:

# install.packages("devtools")
devtools::install_github("mikenguyen13/causalverse")

To install all optional backend packages for full functionality:

library(causalverse)
install_backends()          # install everything
install_backends("did")     # install just DID packages
install_backends("rd")      # install just RD packages

Quick Examples

Synthetic Difference-in-Differences

library(causalverse)

# Estimate treatment effect using multiple methods
setup <- synthdid::panel.matrices(synthdid::california_prop99)
estimates <- panel_estimate(setup, c("sc", "sdid", "did", "sc_ridge"))
process_panel_estimate(estimates)

# Staggered adoption with ATE
ate <- synthdid_est_ate(data = fixest::base_stagg, method = "sdid")
synthdid_plot_ate(ate)

Event Study with Publication-Ready Plots

library(causalverse)

# Sun & Abraham event study
model <- feols(y ~ sunab(year_treated, year) | id + year, data = base_stagg)

# Plot with causalverse theming
plot_event_coefs(model, ref_period = -1) + ama_theme()

RD Robustness Checks

library(causalverse)

# Bandwidth sensitivity analysis
rd_bandwidth_sensitivity(data = df, y = "outcome", x = "running_var", c = 0)

# Placebo cutoff tests
rd_placebo_cutoffs(data = df, y = "outcome", x = "running_var", true_cutoff = 0)

# Covariate balance at the cutoff
rd_covariate_balance(data = df, covariates = c("age", "income"), x = "running_var", c = 0)

Specification Curve Analysis

library(causalverse)

# Run all specifications and visualize
results <- spec_curve(
  data = df,
  y = "outcome",
  x = "treatment",
  controls = list(c("age"), c("age", "income"), c("age", "income", "educ")),
  fixed_effects = list(NULL, "year", c("year", "state"))
)

AMA-Style Publication Plots

library(causalverse)

ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ama_theme() +
  ama_labs(x = "Weight", y = "MPG")

# Export for journal submission
ama_export_fig(filename = "figure1", filepath = "output/")

Citation

Nguyen, M. (2026). causalverse: The All-in-One Causal Inference Toolkit (Version 1.1.0). Zenodo. https://doi.org/10.5281/zenodo.8254063. Retrieved from https://github.com/mikenguyen13/causalverse.