diffindiff: Python library for convenient Difference-in-Differences analyses

This Python library is designed for performing Difference-in-Differences (DiD) analyses in a convenient way. It allows users to construct datasets, define treatment and control groups, and set treatment periods. DiD model analyses may be conducted with both datasets created by built-in functions and ready-to-use external datasets. Both simultaneous and staggered adoption are supported. The library allows for various extensions, such as two-way fixed effects models, group- or individual-specific effects, post-treatment periods, and triple-difference estimations. Additionally, it includes functions for visualizing results, such as plotting DiD coefficients with confidence intervals and illustrating the temporal evolution of staggered treatments. Furthermore, several functions for rigorous treatment setting and data diagnostics are incorporated.

Author

Thomas Wieland ORCID EMail

Availability

• 📦 PyPI: diffindiff
• 💻 GitHub Repository: diffindiff_official
• 📄 DOI (Zenodo): 10.5281/zenodo.18656820

Citation

If you use this software, please cite:

Wieland, T. (2026). diffindiff: A Python library for convenient difference-in-differences analyses (Version 2.2.7) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.18656820

Installation

To install the package, use pip:

pip install diffindiff

To install the package from GitHub with pip:

pip install git+https://github.com/geowieland/diffindiff_official.git

Features

• Data preparation and pre-analysis:
  • Define custom treatment and control groups as well as treatment periods
  • Create ready-to-fit DiD data objects
  • Create predictive counterfactuals
• DiD analysis:
  • Perfom standard DiD analysis
  • Model extensions:
    • Staggered adoption
    • Multiple treatments
    • Two-way fixed effects models
    • Group- or individual-specific treatment effects
    • Group- or individual-specific time trends
    • Including covariates
    • Including after-treatment period
    • Triple Difference (DDD)
    • Own counterfactuals
    • Bonferroni correction for treatment effects
    • Placebo test
• Visualization:
  • Plot observed and expected time course of treatment and control group
  • Plot expected time course of treatment group and counterfactual
  • Plot model coefficients with confidence intervals
  • Plot individual or group-specific treatment effects with confidence intervals
  • Visualize the temporal evolution of staggered treatments
• Diagnosis tools:
  • Test for control conditions
  • Test for type of adoption
  • Test whether the panel dataset is balanced
  • Test for parallel trend assumption

Examples

curfew_DE=pd.read_csv("data/curfew_DE.csv", sep=";", decimal=",")
# Test dataset: Daily and cumulative COVID-19 infections in German counties

curfew_data=create_data(
    outcome_data=curfew_DE,
    unit_id_col="county",
    time_col="infection_date",
    outcome_col="infections_cum_per100000",
    treatment_group= 
        curfew_DE.loc[curfew_DE["Bundesland"].isin([9,10,14])]["county"],
    control_group= 
        curfew_DE.loc[~curfew_DE["Bundesland"].isin([9,10,14])]["county"],
    study_period=["2020-03-01", "2020-05-15"],
    treatment_period=["2020-03-21", "2020-05-05"],
    freq="D"
    )
# Creating DiD dataset by defining groups and treatment time

curfew_data.summary()
# Summary of created treatment data

curfew_model = curfew_data.analysis()
# Model analysis of created data

curfew_model.summary()
# Model summary

curfew_model.plot(
    y_label="Cumulative infections per 100,000",
    plot_title="Curfew effectiveness - Groups over time",
    plot_observed=True
    )
# Plot observed vs. predicted (means) separated by group (treatment and control)

curfew_model.plot_effects(
    x_label="Coefficients with 95% CI",
    plot_title="Curfew effectiveness - DiD effects"
    )
# plot effects

counties_DE=pd.read_csv("data/counties_DE.csv", sep=";", decimal=",", encoding='latin1')
# Dataset with German county data

curfew_data_withgroups = curfew_data.add_covariates(
    additional_df=counties_DE, 
    unit_col="county",
    time_col=None, 
    variables=["BL"])
# Adding federal state column as covariate

curfew_model_withgroups = curfew_data_withgroups.analysis(
    GTE=True,
    group_by="BL")
# Model analysis of created data

curfew_model_withgroups.summary()
# Model summary

curfew_model_withgroups.plot_group_treatment_effects(
    treatment_group_only=True
    )
# Plot of group-specific treatment effects

See the /tests directory for usage examples of most of the included functions.

Literature

• Baker AC, Larcker DF, Wang CCY (2022) How much should we trust staggered difference-in-differences estimates? Journal of Financial Economics 144(2): 370-395. 10.1016/j.jfineco.2022.01.004
• Card D, Krueger AD (1994) Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania. The American Economic Review 84(4): 772-793. JSTOR
• de Haas S, Götz G, Heim S (2022) Measuring the effect of COVID‑19‑related night curfews in a bundled intervention within Germany. Scientific Reports 12: 19732. 10.1038/s41598-022-24086-9
• Goodman-Bacon A (2021) Difference-in-differences with variation in treatment timing. Journal of Econometrics 225(2): 254-277. 10.1016/j.jeconom.2021.03.014
• Greene WH (2012) Econometric Analysis.
• Goldfarb A, Tucker C, Wang Y (2022) Conducting Research in Marketing with Quasi-Experiments. Journal of Marketing 86(3): 1-19. 10.1177/00222429221082977
• Isporhing IE, Lipfert M, Pestel N (2021) Does re-opening schools contribute to the spread of SARS-CoV-2? Evidence from staggered summer breaks in Germany. Journal of Public Economics 198: 104426. 10.1016/j.jpubeco.2021.104426
• Li KT, Luo L, Pattabhiramaiah A (2024) Causal Inference with Quasi-Experimental Data. IMPACT at JMR November 13, 2024. AMA
• Olden A (2018) What do you buy when no one's watching? The effect of self-service checkouts on the composition of sales in retail. Discussion paper FOR 3/18, Norwegian School of Economics, Norway. http://hdl.handle.net/11250/2490886
• Olden A, Moen J (2022) The triple difference estimator. The Econometrics Journal 25(3): 531-553. 10.1093/ectj/utac010
• Strassmann A, Çolak Y, Serra-Burriel M, Nordestgaard BG, Turk A, Afzal S, Puhan MA (2023) Nationwide indoor smoking ban and impact on smoking behaviour and lung function: a two-population natural experiment. Thorax 78(2): 144-150. 10.1136/thoraxjnl-2021-218436
• Villa JM (2016) diff: Simplifying the estimation of difference-in-differences treatment effects. The Stata Journal 16(1): 52-71. 10.1177/1536867X1601600108
• von Bismarck-Osten C, Borusyak K, Schönberg U (2022) The role of schools in transmission of the SARS-CoV-2 virus: quasi-experimental evidence from Germany. Economic Policy 37(109): 87–130. 10.1093/epolic/eiac001
• Wieland T (2025) Assessing the effectiveness of non-pharmaceutical interventions in the SARS-CoV-2 pandemic: results of a natural experiment regarding Baden-Württemberg (Germany) and Switzerland in the second infection wave. Journal of Public Health: From Theory to Practice 33(11): 2497-2511. 10.1007/s10389-024-02218-x
• Wooldridge JM (2012) Introductory Econometrics. A Modern Approach.

What's new (v2.2.7)

• Functions
  • diddata.DiffData.define_treatment() for constructing a new treatment from a column in the dataframe
• Bugfixes:
  • didtools.treatment_times() and didtools.is_multiple_treatment_period() now also identify continuous treatments correctly
  • Fixed problematic type conversion in didtools.fit_metrics()