This guide compares diff-diff with other Python packages for DiD analysis, helping users understand the landscape and choose the right tool.

pyfixest is inspired by R's fixest package and excels at fast fixed effects estimation.

Strengths:

• Very fast for high-dimensional fixed effects (JIT-compiled)
• Sun-Abraham event study estimator
• Gardner's did2s two-stage estimator
• Local projections (Dube et al. 2023)
• Publication-ready tables

Gaps:

• No Callaway-Sant'Anna estimator
• No sensitivity analysis (Honest DiD)
• No synthetic DiD

differences provides a pure Callaway-Sant'Anna implementation.

Strengths:

• Faithful implementation of Callaway & Sant'Anna (2021)
• Multi-valued treatment support
• Triple difference estimation
• Handles unbalanced panels

Gaps:

• No sensitivity analysis
• No synthetic DiD
• Limited inference options (no wild bootstrap)

CausalPy takes a Bayesian approach using PyMC for uncertainty quantification.

Strengths:

• Bayesian credible intervals
• Synthetic control methods
• Nice built-in visualizations
• Interrupted time series

Gaps:

• Limited to simple 2x2 DiD designs
• No staggered treatment support
• No event study methods

linearmodels provides panel data econometrics, serving as a building block for DiD.

Strengths:

• Solid PanelOLS implementation
• Two-way fixed effects
• Various standard error options
• statsmodels-compatible

Gaps:

• No DiD-specific methods (manual implementation required)
• No staggered treatment handling
• No diagnostics or sensitivity analysis

diff-diff fills critical gaps in the Python DiD ecosystem:

1. Only Python library with Honest DiD sensitivity analysis

   Reviewers increasingly expect sensitivity analysis showing robustness to parallel trends violations. diff-diff is the only Python package implementing Rambachan & Roth (2023).

2. Unified toolkit

   Combines Callaway-Sant'Anna, Synthetic DiD, TWFE, and Honest DiD in one package with a consistent API.

3. Built-in diagnostics

   Placebo tests, parallel trends tests, Goodman-Bacon decomposition, and sensitivity analysis out of the box.

4. sklearn-style API

   Familiar fit() interface with rich result objects.

# diff-diff
from diff_diff import DifferenceInDifferences

did = DifferenceInDifferences()
results = did.fit(data, outcome='y', treatment='treated', time='post')
print(results.summary())

# pyfixest
import pyfixest as pf

fit = pf.feols("y ~ treated:post | unit + time", data=data)
fit.summary()

# linearmodels
from linearmodels.panel import PanelOLS

mod = PanelOLS.from_formula(
    'y ~ treated:post + EntityEffects + TimeEffects',
    data=data.set_index(['unit', 'time'])
)
results = mod.fit(cov_type='clustered', cluster_entity=True)

# diff-diff
from diff_diff import CallawaySantAnna

cs = CallawaySantAnna(estimation_method='dr')
results = cs.fit(
    data,
    outcome='y',
    unit='unit',
    time='time',
    first_treat='first_treat',
    covariates=['x1', 'x2'],
    aggregate='event_study'
)
event_study = results.event_study_effects

# differences
from differences import ATTgt

att = ATTgt(
    data=data,
    outcome='y',
    cohort='first_treat',
    time='time',
    strata='unit'
)
att.fit()
att.aggregate('event')

# diff-diff (only Python option)
from diff_diff import HonestDiD, plot_sensitivity

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

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

# No equivalent in other Python packages

Use diff-diff when:

• You need sensitivity analysis (Honest DiD)
• You want Callaway-Sant'Anna with doubly robust estimation
• You need built-in diagnostics (placebo tests, parallel trends)
• You prefer a unified sklearn-style API

Use pyfixest when:

• Speed is critical (high-dimensional fixed effects)
• You need Sun-Abraham or did2s estimators
• You want R's fixest-style syntax
• You need local projections

Use differences when:

• You need multi-valued treatment effects
• You need triple difference estimation
• You want the most faithful CS implementation

Use CausalPy when:

• You want Bayesian uncertainty quantification
• You're doing synthetic control (not synthetic DiD)
• You prefer PyMC-based modeling

Use linearmodels when:

• You're doing general panel econometrics beyond DiD
• You need statsmodels compatibility
• You're implementing DiD manually with full control