Comparison with R Packages

This guide compares diff-diff with popular R packages for DiD analysis, helping users familiar with R transition to Python.

Overview

Feature	diff-diff (Python)	did (R)	Other R
Basic DiD	✅ `DifferenceInDifferences`	✅ `att_gt`	✅ `fixest::feols`
Staggered DiD	✅ `CallawaySantAnna`	✅ `att_gt`	`did2s`, `DRDID`
Covariate adjustment	✅ DR, IPW, Reg	✅ DR, IPW, Reg	✅ Varies
Honest DiD	✅ `HonestDiD`	`HonestDiD` package	N/A
Synthetic DiD	✅ `SyntheticDiD`	`synthdid` package	N/A
Wild bootstrap	✅ `wild_bootstrap_se`	`fwildclusterboot`	N/A

Package Correspondence

R `did` Package → diff-diff

The R did package by Callaway and Sant’Anna is the gold standard for staggered DiD. Here’s how to translate common operations:

Basic estimation:

# 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'
)

With covariates (doubly robust):

# 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']
)

Aggregations:

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

# Python
overall_att = results.att  # Simple aggregation
event_study = results.aggregate('event_time')  # Dynamic
by_group = results.aggregate('group')  # By cohort

R `HonestDiD` Package → diff-diff

The HonestDiD package implements Rambachan & Roth (2023) sensitivity analysis:

Relative magnitudes (ΔRM):

# 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, DeltaRM

honest = HonestDiD(delta=DeltaRM(M_bar=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]
)

Smoothness restrictions (ΔSD):

# 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, DeltaSD

honest = HonestDiD(delta=DeltaSD(M=0.05))
results = honest.fit(event_study_results)

R `synthdid` Package → diff-diff

The synthdid package implements Arkhangelsky et al. (2021):

# 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

sdid = SyntheticDiD()
results = sdid.fit(
    data,
    outcome='Y',
    unit='unit',
    time='time',
    treated='treatment',
    treatment_start=T0
)

Key Differences

Design Philosophy

diff-diff: sklearn-style API with fit() method, returning rich result objects
R packages: Function-based, returning lists or S3/S4 objects

Inference

diff-diff: Analytical SEs by default, wild bootstrap available
R did: Multiplier bootstrap by default

Fixed Effects

diff-diff: absorb parameter for high-dimensional FE (within transformation)
R fixest: feols with | notation for absorbed FE

Output Format

diff-diff results have convenience methods:

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

Feature Comparison Table

Feature	diff-diff	R did	R HonestDiD	R synthdid
Basic 2x2 DiD	✅	✅	❌	❌
TWFE	✅	❌	❌	❌
Staggered DiD (CS)	✅	✅	❌	❌
Covariate adjustment	✅	✅	❌	❌
Doubly robust	✅	✅	❌	❌
Group-time effects	✅	✅	❌	❌
Event study	✅	✅	✅	❌
Synthetic DiD	✅	❌	❌	✅
Honest DiD (ΔRM)	✅	❌	✅	❌
Honest DiD (ΔSD)	✅	❌	✅	❌
Wild bootstrap	✅	❌	❌	❌
Cluster-robust SE	✅	✅	❌	✅
Placebo tests	✅	❌	❌	✅
Parallel trends tests	✅	✅	❌	❌
Bacon decomposition	✅	❌	❌	❌

Migration Tips

Column names: diff-diff uses string column names, similar to R packages
Formula interface: diff-diff supports R-style formulas for basic DiD: formula='y ~ treated * post'
Results access: Use .att, .se, .ci instead of $att, $se
Visualization: plot_event_study() produces matplotlib figures similar to ggdid() output
Missing data: diff-diff requires complete data; use balance_panel() or dropna() first