did-had 0.2.1

Difference-in-Differences with Heterogeneous Adoption Design (HAD) estimator

did-had

A Python implementation of the Heterogeneous Adoption Design (HAD) estimator for difference-in-differences analysis from de Chaisemartin et al. (2025). Computes heterogeneity-robust DID estimators, in heterogeneous adoption designs where all groups start receiving heterogeneous treatment doses at the same date and no group remains fully untreated. In such designs, all groups experience their first treatment change at the same date (there are no stayers), so _stat and _dyn cannot be used.

Overview

The did-had package implements the HAD estimator for settings where:

• All groups receive treatment but with different intensities (no pure control group)
• Treatment adoption occurs at the same time for all groups, but doses vary
• Some groups have treatment doses close to zero, serving as "quasi-untreated" groups

This is a Python port of the official Stata package did_had, producing numerically identical results.

Installation

pip install nprobust
pip install did-had

Quick Start

import pandas as pd
from did_had import DidHad

# Load your panel data
df = pd.read_stata("tutorial_data.dta")

# Create and fit the model
model = DidHad(kernel="tri")
results = model.fit(
    df=df,
    outcome="y",        # outcome variable
    group="g",          # group identifier
    time="t",           # time period
    treatment="d",      # treatment dose
    effects=5,          # post-treatment periods
    placebo=4           # pre-treatment placebos
)

# View results
print(results)

# Get ATT
print(f"Average Treatment Effect: {results.att():.4f}")

# Save results
model.save_results("results.csv", format="csv")

Example Output

===========================================================================
DID-HAD Estimation Results
===========================================================================
Number of groups: 1,000
Number of periods: 10
Adoption period (F): 6.0
Kernel: tri
Confidence level: 95%

---------------------------------------------------------------------------
                          Effect Estimates                      QUG* Test
         --------------------------------------------------- ---------------
          Estimate       SE     LB.CI     UB.CI     N      BW    N.BW        T    p.val
Effect_1   4.28198  0.55814   2.71751   4.90538 1,000 0.36133    371  3.96182  0.20154
Effect_2   3.59260  0.66675   2.02563   4.63925 1,000 0.27104    282  3.96182  0.20154
Effect_3   4.25466  0.67544   2.71354   5.36123 1,000 0.31407    324  3.96182  0.20154
...

Features

• Exact replication of Stata's did_had command
• Bias-corrected inference using local polynomial regression (lprobust-style)
• Quasi-untreated group tests to validate the estimation strategy
• Event-study plots for visualization
• Multiple output formats: CSV, Stata, Excel, pickle

API Reference

DidHad Class

DidHad(kernel="epa", alpha=0.05, nnmatch=3)

Parameters:

• kernel: Kernel function - "epa" (Epanechnikov), "tri" (triangular), "uni" (uniform), "gau" (Gaussian)
• alpha: Significance level for confidence intervals (default: 0.05 for 95% CI)
• nnmatch: Number of nearest neighbors for variance estimation

fit() Method

results = model.fit(
    df,                      # Panel DataFrame
    outcome,                 # Outcome variable name
    group,                   # Group identifier name
    time,                    # Time period name
    treatment,               # Treatment dose name
    effects=1,               # Number of post-treatment periods
    placebo=0,               # Number of pre-treatment placebos
    dynamic=False,           # Use cumulative treatment dose
    bandwidth=None,          # Global bandwidth (or Silverman if None)
    bandwidth_effect=None,   # Effect-specific bandwidths (dict or scalar)
    bandwidth_placebo=None   # Placebo-specific bandwidths (dict or scalar)
)

DidHadResults Object

results.summary()           # Formatted summary string
results.to_dataframe()      # Full results as DataFrame
results.att()               # Average treatment effect on treated
results.effects             # DataFrame of effect estimates
results.placebos            # DataFrame of placebo estimates

Plotting

# Basic plot
model.plot()

# Customized plot
model.plot(
    figsize=(10, 6),
    title="My Event Study",
    xlabel="Periods since treatment",
    ylabel="Treatment Effect",
    show_ci=True
)

Saving Results

model.save_results("results.csv", format="csv")
model.save_results("results.dta", format="stata")
model.save_results("results.xlsx", format="excel")
model.save_results("results.pkl", format="pickle")

Comparison with Stata

This package produces numerically identical results to the official Stata did_had command when using the same bandwidths:

Estimate	Python	Stata	Match
Effect_1	4.28198	4.28198	Yes
Effect_2	3.59260	3.59260	Yes
Effect_3	4.25466	4.25466	Yes
...	...	...	...

Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• Pandas >= 1.3.0
• Matplotlib >= 3.4.0 (optional, for plotting)

References

• de Chaisemartin, C., D'Haultfoeuille, X., Pasquier, F., & Vazquez-Bare, G. (2025). "Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period". SSRN

• Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). "nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference". Journal of Statistical Software.

License

MIT License. See LICENSE for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.