R Comparison

Overview

Package Correspondence

R did Package → diff-diff

# R (did package)
library(did)
out <- att_gt(
  yname = "Y",
  tname = "period",
  idname = "id",
  gname = "G",
  data = data
)

# Python (diff-diff)
from diff_diff import CallawaySantAnna

cs = CallawaySantAnna()
results = cs.fit(
    data,
    outcome='Y',
    time='period',
    unit='id',
    first_treat='G'
)

# R
out <- att_gt(
  yname = "Y", tname = "period",
  idname = "id", gname = "G",
  xformla = ~ X1 + X2,
  est_method = "dr",
  data = data
)

# Python
cs = CallawaySantAnna(estimation_method='dr')
results = cs.fit(
    data,
    outcome='Y',
    time='period',
    unit='id',
    first_treat='G',
    covariates=['X1', 'X2']
)

# R
agg_simple <- aggte(out, type = "simple")
agg_dynamic <- aggte(out, type = "dynamic")
agg_group <- aggte(out, type = "group")

# Python (unlike R's aggte(), aggregation is requested at fit time)
results = cs.fit(data, outcome='Y', time='period', unit='id',
                 first_treat='G', aggregate='all')
overall_att = results.overall_att  # Simple aggregation
event_study = results.event_study_effects  # Dynamic
by_group = results.group_effects  # By cohort

R HonestDiD Package → diff-diff

# R
library(HonestDiD)
delta_rm_results <- createSensitivityResults_relativeMagnitudes(
  betahat = beta_hat,
  sigma = sigma,
  numPrePeriods = 4,
  numPostPeriods = 3,
  Mbarvec = seq(0, 2, by = 0.5)
)

# Python
from diff_diff import HonestDiD

honest = HonestDiD(method='relative_magnitude', M=1.0)
results = honest.fit(event_study_results)

# Sensitivity analysis over M grid
sensitivity = honest.sensitivity_analysis(
    event_study_results,
    M_grid=[0, 0.5, 1.0, 1.5, 2.0]
)

# R
delta_sd_results <- createSensitivityResults(
  betahat = beta_hat,
  sigma = sigma,
  numPrePeriods = 4,
  numPostPeriods = 3,
  Mvec = seq(0, 0.1, by = 0.02)
)

# Python
from diff_diff import HonestDiD

honest = HonestDiD(method='smoothness', M=0.05)
results = honest.fit(event_study_results)

R synthdid Package → diff-diff

# R
library(synthdid)
setup <- panel.matrices(data, unit = "unit", time = "time",
                        outcome = "Y", treatment = "treatment")
tau.hat <- synthdid_estimate(setup$Y, setup$N0, setup$T0)

# Python
from diff_diff import SyntheticDiD

# SyntheticDiD requires a time-invariant ever-treated indicator
data['ever_treated'] = data.groupby('unit')['treatment'].transform('max')

# Derive post-treatment periods from treatment timing
post_periods = sorted(data.loc[data['treatment'] == 1, 'time'].unique())

sdid = SyntheticDiD()
results = sdid.fit(
    data,
    outcome='Y',
    unit='unit',
    time='time',
    treatment='ever_treated',
    post_periods=post_periods
)

Heterogeneous Adoption (HAD)

import numpy as np
import pandas as pd
from diff_diff import HeterogeneousAdoptionDiD

# Build a HAD-shape panel: D=0 in pre-periods (t < F), D > 0 only at F+.
rng = np.random.default_rng(42)
G, F, T = 200, 4, 5
doses = rng.beta(0.5, 1.0, size=G)
rows = []
for g in range(G):
    for t in range(1, T + 1):
        y = (rng.normal()
             + (doses[g] + doses[g] ** 2) * (t >= F)
             + rng.normal(0, 0.5))
        d = doses[g] if t >= F else 0.0
        rows.append({'unit': g, 'period': t, 'y': y, 'dose': d})
had_data = pd.DataFrame(rows)

est = HeterogeneousAdoptionDiD()
results = est.fit(had_data, outcome_col='y', unit_col='unit',
                  time_col='period', dose_col='dose',
                  aggregate='event_study')

Key Differences

Design Philosophy

Inference

Fixed Effects

Output Format

results.summary()       # Print formatted table
results.to_dict()       # Dictionary representation
results.to_dataframe()  # pandas DataFrame

Feature Comparison Table

Note

R equivalents for estimators not covered by the did, HonestDiD, or synthdid packages: Sun-Abraham is available via fixest::sunab(); Imputation DiD via the didimputation package; Two-Stage DiD via the did2s package; Bacon Decomposition via the bacondecomp package; Stacked DiD requires manual implementation or the stackedev package; Continuous DiD is available via the did package continuous extension; Triple Difference requires manual implementation in R. TROP and Efficient DiD have no direct R equivalents. HeterogeneousAdoptionDiD (dCDH 2026) overlaps with the dedicated R package DIDHAD (de Chaisemartin et al., 2025), which covers the QUG case (Design 1'); diff-diff additionally covers Design 1 (no QUG, WAS_{d_lower}) and survey-design integration via Binder TSL.

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