lwdid - Difference-in-Differences with Rolling Transformations

Python implementation of the Lee and Wooldridge difference-in-differences methods for panel data, supporting both small cross-sectional sample sizes with exact inference and large-sample settings with asymptotic inference.

The package implements a simple transformation approach that converts panel DiD estimation into cross-sectional treatment effects problems, enabling various estimators including regression adjustment, inverse probability weighting, doubly robust methods, and propensity score matching.

Python Version License

Supported Scenarios

Small-Sample Common Timing (Lee and Wooldridge 2026)

For settings with small numbers of treated or control units and common treatment timing. Under classical linear model assumptions (normality and homoskedasticity), exact t-based finite-sample inference is available.

Large-Sample Common Timing (Lee and Wooldridge 2025)

For settings with larger cross-sectional samples. Supports heteroskedasticity- robust (HC0-HC4) and cluster-robust standard errors with asymptotic inference.

Staggered Adoption (Lee and Wooldridge 2025)

For settings where units are treated at different times. Estimates cohort-time specific ATTs with flexible control group strategies (never-treated or not-yet- treated units) and multiple aggregation options.

Key Features

  • Small-sample inference: Exact t-based inference under CLM assumptions

  • Large-sample inference: HC0-HC4 heteroskedasticity-robust and cluster-robust options

  • Staggered adoption: Full support for staggered treatment timing with cohort-time effects

  • Four transformation methods: demean, detrend, demeanq, detrendq

  • Multiple estimators: Regression adjustment (RA), IPW, IPWRA, PSM

  • Randomization inference: Bootstrap and permutation-based procedures

  • Period-specific effects: Separate ATT estimates for each post-treatment period

  • Event study visualization: Built-in plotting for staggered designs

  • Control variables: Time-invariant covariates with automatic centering

Quick Start

Installation

pip install lwdid

Common Timing Example

import pandas as pd
from lwdid import lwdid

# Load panel data
data = pd.read_csv('smoking.csv')

# Estimate ATT with small-sample inference
results = lwdid(
    data,
    y='lcigsale',      # outcome variable
    d='d',             # treatment indicator
    ivar='state',      # unit identifier
    tvar='year',       # time variable
    post='post',       # post-treatment indicator
    rolling='detrend', # transformation method
)

print(results.summary())

Staggered Adoption Example

# Load staggered adoption data
data = pd.read_csv('castle.csv')

# Estimate with staggered design
results = lwdid(
    data,
    y='lhomicide',
    ivar='state',
    tvar='year',
    gvar='effyear',        # first treatment period
    rolling='demean',
    aggregate='overall',   # aggregate to overall effect
    control_group='not_yet_treated',
)

print(results.summary())
results.plot_event_study()

Documentation

User Guide

API Reference

Advanced Topics

Development

References

Lee, S. J., and Wooldridge, J. M. (2026). Simple Approaches to Inference with Difference-in-Differences Estimators with Small Cross-Sectional Sample Sizes. Available at SSRN 5325686.

Lee, S. J., and Wooldridge, J. M. (2025). A Simple Transformation Approach to Difference-in-Differences Estimation for Panel Data. Available at SSRN 4516518.