Python Comparison#

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

Overview#

Feature

diff-diff

pyfixest

differences

CausalPy

linearmodels

Basic DiD

✅

✅

✅

✅

✅ (manual)

TWFE

✅

✅

❌

❌

✅

Callaway-Sant’Anna

✅

❌

✅

❌

❌

Sun-Abraham

✅

✅

❌

❌

❌

Synthetic DiD

✅

❌

❌

✅ (SC only)

❌

Honest DiD

✅

❌

❌

❌

❌

Wild bootstrap

✅

✅

❌

❌

❌

Package Summaries#

pyfixest#

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#

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#

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#

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

Why diff-diff?#

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.

Feature Comparison Table#

Feature

diff-diff

pyfixest

differences

CausalPy

linearmodels

Basic 2x2 DiD

✅

✅

✅

✅

✅

Two-way fixed effects

✅

✅

❌

❌

✅

Staggered DiD (CS)

✅

❌

✅

❌

❌

Sun-Abraham estimator

✅

✅

❌

❌

❌

Gardner’s did2s

✅

✅

❌

❌

❌

Local projections

❌

✅

❌

❌

❌

Synthetic DiD

✅

❌

❌

❌

❌

Synthetic control

❌

❌

❌

✅

❌

Covariate adjustment

✅

✅

✅

Limited

✅

Doubly robust

✅

❌

✅

❌

❌

IPW

✅

❌

✅

❌

❌

Group-time effects

✅

❌

✅

❌

❌

Event study aggregation

✅

✅

✅

❌

❌

Honest DiD (ΔRM)

✅

❌

❌

❌

❌

Honest DiD (ΔSD)

✅

❌

❌

❌

❌

Sensitivity analysis

✅

❌

❌

❌

❌

Wild bootstrap

✅

✅

❌

❌

❌

Cluster-robust SE

✅

✅

✅

❌

✅

Placebo tests

✅

❌

❌

❌

❌

Parallel trends tests

✅

❌

❌

❌

❌

Bacon decomposition

✅

❌

❌

❌

❌

Event study plots

✅

✅

✅

✅

❌

Triple Difference (DDD)

✅

❌

✅

❌

❌

TROP

✅

❌

❌

❌

❌

Stacked DiD

✅

❌

❌

❌

❌

Bacon Decomposition

✅

❌

❌

❌

❌

Continuous DiD

✅

❌

❌

❌

❌

Efficient DiD

✅

❌

❌

❌

❌

Built-in datasets

✅

❌

❌

❌

❌

Bayesian inference

❌

❌

❌

✅

❌

Code Comparison#

Basic DiD#

# 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)

Staggered DiD (Callaway-Sant’Anna)#

# 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')

Sensitivity Analysis#

# 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

When to Use Each Package#

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