Package index • causalverse

Reporting & Themes

Publication-ready AMA-style themes, labels, and export utilities.

ama_theme(): Custom Theme for ggplot2: American Marketing Association Style

ama_labs(): Custom Label Formatting for ggplot2: American Marketing Association Style

ama_scale_color(): Custom Color Scale for ggplot2: American Marketing Association Style

ama_scale_fill(): Custom Fill Scale for ggplot2: American Marketing Association Style

ama_export_fig(): Function to export a figure with custom settings

ama_export_tab(): Function to export a table with AMA style

nice_tab(): Nice Tabulation Function

causal_table(): Publication-Ready Causal Inference Results Table

Unified Tidy Output

Standardized extraction of estimates from any causal model.

tidy_causal(): Tidy Output from Causal Inference Model Objects

compare_estimators(): Compare Causal Estimators in a Formatted Table

effect_size_convert(): Convert Between Causal Effect Size Metrics

causal_summary(): Unified Causal Model Summary

Synthetic DID & Synthetic Controls

Estimation, inference, and visualization for synthetic control methods.

synthdid_est(): Synthetic DID Estimation Using synthdid Package

synthdid_est_ate(): Estimate the SynthDiD ATEs and Standard Errors

synthdid_est_per(): Estimate Treatment Effects for Each Period

synthdid_plot_ate(): Create ATE Plot Using ggplot2

synthdid_se_jacknife(): Calculate Jackknife Standard Errors for Synthetic DID

synthdid_se_placebo(): Calculate Placebo Standard Errors for Synthetic DID

panel_estimate(): Panel Estimate Function

process_panel_estimate(): Process Panel Estimate

sc_gap_plot(): Gap Plot for Synthetic Control Analysis

sc_inference_plot(): Synthetic Control Gaps and Inference Plot

Difference-in-Differences

Visualization, testing, power analysis, and sensitivity for DiD designs.

plot_par_trends(): Plot Parallel Trends

plot_coef_par_trends(): Plot Coefficients of Parallel Trends

plot_treat_time(): Plot number of treated units over time or return a dataframe.

plot_trends_across_group(): Custom Faceted Line Plot with Optional Standard Error

plot_covariate_balance_pretrend(): Plot Covariate Balance Over Pre-Treatment Period

stack_data(): Stacked Data for Staggered DiD Analysis

get_balanced_panel(): Extract a Balanced Panel

plot_panel_estimate() plot_PanelEstimate(): Plot Estimated Effects of Treatment Over Time

test_pretrends(): Unified Pre-Trends Testing for Event Study Models

did_event_study(): Enhanced Event Study Plot for Difference-in-Differences

did_power_analysis(): Power Analysis for Difference-in-Differences Designs

staggered_summary(): Summarize Staggered Adoption Panel Designs

treatment_calendar(): Treatment Calendar Heatmap for Staggered Adoption Designs

bacon_decomp_plot(): Plot Bacon Decomposition of TWFE Estimates

pretrend_sensitivity(): Pre-trend Sensitivity Analysis (HonestDiD)

parallel_trends_plot(): Parallel Trends Plot for DiD

stacked_did(): Stacked Difference-in-Differences

Randomized Experiments

Power analysis, balance checking, doubly-robust estimation, and attrition.

mde_calc(): Minimum Detectable Effect (MDE) Calculator

dr_ate(): Doubly-Robust Augmented IPW Estimator for the ATE and ATT

placebo_test(): Randomization Inference and Placebo Tests for Treatment Effects

dose_response(): Dose-Response Estimation for Continuous Treatments

dose_response_curve(): Dose-Response Curve for Continuous Treatment

attrition_analysis(): Attrition Analysis for Experimental & Panel Data

power_sim(): Monte Carlo Power Simulation for Causal Designs

Heterogeneous Treatment Effects

CATE estimation, BLP, GATES, Qini curves, forest summaries, and subgroup plots.

blp_analysis(): Best Linear Predictor (BLP) Analysis for Conditional Average Treatment Effects

qini_curve(): Qini Curve and AUUC for Uplift / Policy Evaluation

causal_forest_summary(): Comprehensive Summary of Causal Forest Results

heterogeneity_plot(): Treatment Effect Heterogeneity Visualization

heterogeneity_forest_plot(): Heterogeneity Forest Plot

Balance & Matching

Covariate balance tables, Love plots, overlap diagnostics, and balance plots.

balance_assessment(): Assess balance between treated and control groups

balance_scatter_custom(): Custom function to visualize the balance between treatment and control groups

balance_table(): Publication-Ready Covariate Balance Table

balance_plot(): Comprehensive Balance Plot Suite

covariate_summary(): Comprehensive Covariate Summary Table (Table 1)

love_plot(): Love Plot for Covariate Balance Visualization

plot_density_by_treatment(): Plot Density by Treatment

plot_pscore_overlap(): Propensity Score Overlap Diagnostic Plot

overlap_weights(): Overlap Weights and Trimming for Propensity Score Analysis

propensity_diagnostics(): Propensity Score Diagnostics Panel

Mediation Analysis

Causal mediation decomposition (ACME, ADE).

mediation_analysis(): Causal Mediation Analysis with ACME and ADE Estimation

lee_bounds(): Summarize Lee Bounds for Always-Takers

med_ind(): Estimate Mediation Indirect Effects

Robustness & Sensitivity

Specification curves, multiverse analysis, sensitivity plots, exclusion restriction tests.

spec_curve(): Specification Curve Analysis

sensitivity_plot(): Plot Treatment Effect Sensitivity to Unobserved Confounding

iv_sensitivity(): Sensitivity Analysis for Instrumental Variables: Exclusion Restriction

multiverse_analysis(): Multiverse / Specification Curve Analysis

confounding_strength(): Sensitivity Analysis for Omitted Variable Bias (E-value & RV)

Visualization (General)

Coefficient plots, event study plots, and HTE visualization.

plot_event_coefs(): Plot Event Study Coefficients from Multiple Estimators

plot_coef_comparison(): Compare Treatment Effect Estimates Across Models

coef_plot(): Universal Coefficient Plot

event_study_finance(): Compute Abnormal Returns and CARs for Finance Event Studies

Regression Discontinuity

RD estimation, bandwidth sensitivity, placebo tests, and assumption checks.

plot_rd_aa_share(): Plot RD Always-assigned Share

rd_plot(): Publication-Ready Regression Discontinuity Plot

rd_bandwidth_sensitivity(): RD Bandwidth Sensitivity Analysis

rd_covariate_balance(): RD Covariate Balance Test

rd_placebo_cutoffs(): RD Placebo Cutoff Test

rd_assumption_tests(): Comprehensive RD Assumption Tests

rd_binscatter(): Binned Scatter Plot for RD Analysis

Instrumental Variables

First-stage diagnostics, sensitivity, and weak-IV robust inference.

iv_diagnostic_summary(): Instrumental Variable Diagnostic Summary

iv_diagnostics(): Instrumental Variables Diagnostics

Panel Data Diagnostics

Unit root, serial correlation, cross-sectional dependence, and heteroskedasticity tests.

panel_diagnostics(): Panel Data Diagnostic Tests

Causal DAG Utilities

DAG visualization, adjustment set identification, and d-separation implication testing.

dag_plot(): Quick Causal DAG Visualization

dag_adjustment_sets(): List Adjustment Sets for a DAG

dag_test_implications(): Test Conditional Independence Implied by a DAG

Package Management

Install and check optional backend packages.

install_backends(): Install Backend Packages for Causal Inference Methods

check_backends(): List Available Backend Packages and Their Status