diff-diff 0.1.0

A library for Difference-in-Differences causal inference analysis

diff-diff

A Python library for Difference-in-Differences (DiD) causal inference analysis with an sklearn-like API and statsmodels-style outputs.

Installation

pip install diff-diff

Or install from source:

git clone https://github.com/igerber/diff-diff.git
cd diff-diff
pip install -e .

Quick Start

import pandas as pd
from diff_diff import DifferenceInDifferences

# Create sample data
data = pd.DataFrame({
    'outcome': [10, 11, 15, 18, 9, 10, 12, 13],
    'treated': [1, 1, 1, 1, 0, 0, 0, 0],
    'post': [0, 0, 1, 1, 0, 0, 1, 1]
})

# Fit the model
did = DifferenceInDifferences()
results = did.fit(data, outcome='outcome', treatment='treated', time='post')

# View results
print(results)  # DiDResults(ATT=3.5000*, SE=1.2583, p=0.0367)
results.print_summary()

Output:

======================================================================
          Difference-in-Differences Estimation Results
======================================================================

Observations:                        8
Treated units:                       4
Control units:                       4
R-squared:                      0.9123

----------------------------------------------------------------------
Parameter         Estimate     Std. Err.     t-stat      P>|t|
----------------------------------------------------------------------
ATT                 3.5000       1.2583      2.782      0.0367
----------------------------------------------------------------------

95% Confidence Interval: [0.3912, 6.6088]

Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1
======================================================================

Features

• sklearn-like API: Familiar fit() interface with get_params() and set_params()
• Pythonic results: Easy access to coefficients, standard errors, and confidence intervals
• Multiple interfaces: Column names or R-style formulas
• Robust inference: Heteroskedasticity-robust (HC1) and cluster-robust standard errors
• Panel data support: Two-way fixed effects estimator for panel designs

Usage

Basic DiD with Column Names

from diff_diff import DifferenceInDifferences

did = DifferenceInDifferences(robust=True, alpha=0.05)
results = did.fit(
    data,
    outcome='sales',
    treatment='treated',
    time='post_policy'
)

# Access results
print(f"ATT: {results.att:.4f}")
print(f"Standard Error: {results.se:.4f}")
print(f"P-value: {results.p_value:.4f}")
print(f"95% CI: {results.conf_int}")
print(f"Significant: {results.is_significant}")

Using Formula Interface

# R-style formula syntax
results = did.fit(data, formula='outcome ~ treated * post')

# Explicit interaction syntax
results = did.fit(data, formula='outcome ~ treated + post + treated:post')

# With covariates
results = did.fit(data, formula='outcome ~ treated * post + age + income')

Including Covariates

results = did.fit(
    data,
    outcome='outcome',
    treatment='treated',
    time='post',
    covariates=['age', 'income', 'education']
)

Fixed Effects

Use fixed_effects for low-dimensional categorical controls (creates dummy variables):

# State and industry fixed effects
results = did.fit(
    data,
    outcome='sales',
    treatment='treated',
    time='post',
    fixed_effects=['state', 'industry']
)

# Access fixed effect coefficients
state_coefs = {k: v for k, v in results.coefficients.items() if k.startswith('state_')}

Use absorb for high-dimensional fixed effects (more efficient, uses within-transformation):

# Absorb firm-level fixed effects (efficient for many firms)
results = did.fit(
    data,
    outcome='sales',
    treatment='treated',
    time='post',
    absorb=['firm_id']
)

Combine covariates with fixed effects:

results = did.fit(
    data,
    outcome='sales',
    treatment='treated',
    time='post',
    covariates=['size', 'age'],           # Linear controls
    fixed_effects=['industry'],            # Low-dimensional FE (dummies)
    absorb=['firm_id']                     # High-dimensional FE (absorbed)
)

Cluster-Robust Standard Errors

did = DifferenceInDifferences(cluster='state')
results = did.fit(
    data,
    outcome='outcome',
    treatment='treated',
    time='post'
)

Two-Way Fixed Effects (Panel Data)

from diff_diff.estimators import TwoWayFixedEffects

twfe = TwoWayFixedEffects()
results = twfe.fit(
    panel_data,
    outcome='outcome',
    treatment='treated',
    time='year',
    unit='firm_id'
)

Working with Results

Export Results

# As dictionary
results.to_dict()
# {'att': 3.5, 'se': 1.26, 'p_value': 0.037, ...}

# As DataFrame
df = results.to_dataframe()

Check Significance

if results.is_significant:
    print(f"Effect is significant at {did.alpha} level")

# Get significance stars
print(f"ATT: {results.att}{results.significance_stars}")
# ATT: 3.5000*

Access Full Regression Output

# All coefficients
results.coefficients
# {'const': 9.5, 'treated': 1.0, 'post': 2.5, 'treated:post': 3.5}

# Variance-covariance matrix
results.vcov

# Residuals and fitted values
results.residuals
results.fitted_values

# R-squared
results.r_squared

Checking Assumptions

Parallel Trends

Simple slope-based test:

from diff_diff.utils import check_parallel_trends

trends = check_parallel_trends(
    data,
    outcome='outcome',
    time='period',
    treatment_group='treated'
)

print(f"Treated trend: {trends['treated_trend']:.4f}")
print(f"Control trend: {trends['control_trend']:.4f}")
print(f"Difference p-value: {trends['p_value']:.4f}")

Robust distributional test (Wasserstein distance):

from diff_diff.utils import check_parallel_trends_robust

results = check_parallel_trends_robust(
    data,
    outcome='outcome',
    time='period',
    treatment_group='treated',
    unit='firm_id',              # Unit identifier for panel data
    pre_periods=[2018, 2019],    # Pre-treatment periods
    n_permutations=1000          # Permutations for p-value
)

print(f"Wasserstein distance: {results['wasserstein_distance']:.4f}")
print(f"Wasserstein p-value: {results['wasserstein_p_value']:.4f}")
print(f"KS test p-value: {results['ks_p_value']:.4f}")
print(f"Parallel trends plausible: {results['parallel_trends_plausible']}")

The Wasserstein (Earth Mover's) distance compares the full distribution of outcome changes, not just means. This is more robust to:

• Non-normal distributions
• Heterogeneous effects across units
• Outliers

Equivalence testing (TOST):

from diff_diff.utils import equivalence_test_trends

results = equivalence_test_trends(
    data,
    outcome='outcome',
    time='period',
    treatment_group='treated',
    unit='firm_id',
    equivalence_margin=0.5       # Define "practically equivalent"
)

print(f"Mean difference: {results['mean_difference']:.4f}")
print(f"TOST p-value: {results['tost_p_value']:.4f}")
print(f"Trends equivalent: {results['equivalent']}")

API Reference

DifferenceInDifferences

DifferenceInDifferences(
    robust=True,      # Use HC1 robust standard errors
    cluster=None,     # Column for cluster-robust SEs
    alpha=0.05        # Significance level for CIs
)

Methods:

Method	Description
fit(data, outcome, treatment, time, ...)	Fit the DiD model
summary()	Get formatted summary string
print_summary()	Print summary to stdout
get_params()	Get estimator parameters (sklearn-compatible)
set_params(**params)	Set estimator parameters (sklearn-compatible)

fit() Parameters:

Parameter	Type	Description
data	DataFrame	Input data
outcome	str	Outcome variable column name
treatment	str	Treatment indicator column (0/1)
time	str	Post-treatment indicator column (0/1)
formula	str	R-style formula (alternative to column names)
covariates	list	Linear control variables
fixed_effects	list	Categorical FE columns (creates dummies)
absorb	list	High-dimensional FE (within-transformation)

DiDResults

Attributes:

Attribute	Description
att	Average Treatment effect on the Treated
se	Standard error of ATT
t_stat	T-statistic
p_value	P-value for H0: ATT = 0
conf_int	Tuple of (lower, upper) confidence bounds
n_obs	Number of observations
n_treated	Number of treated units
n_control	Number of control units
r_squared	R-squared of regression
coefficients	Dictionary of all coefficients
is_significant	Boolean for significance at alpha
significance_stars	String of significance stars

Methods:

Method	Description
summary(alpha)	Get formatted summary string
print_summary(alpha)	Print summary to stdout
to_dict()	Convert to dictionary
to_dataframe()	Convert to pandas DataFrame

Requirements

• Python >= 3.9
• numpy >= 1.20
• pandas >= 1.3
• scipy >= 1.7

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black diff_diff tests
ruff check diff_diff tests

License

MIT License