diff-diff

diff-diff: Difference-in-Differences causal inference in Python - sklearn-like API with Callaway-Sant'Anna, Synthetic DiD, Honest DiD, and Event Studies

PyPI version Python versions License: MIT Downloads DOI

A Python library for Difference-in-Differences (DiD) causal inference - sklearn-like estimators with statsmodels-style outputs, built for econometricians, marketing analysts, and data scientists running campaign-lift, policy, and staggered-rollout analyses.

Installation

pip install diff-diff

For development:

git clone https://github.com/igerber/diff-diff.git
cd diff-diff
pip install -e ".[dev]"

Quick Start

import pandas as pd
from diff_diff import DifferenceInDifferences  # or: DiD

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],
})

did = DifferenceInDifferences()
results = did.fit(data, outcome='outcome', treatment='treated', time='post')
print(results)              # DiDResults(ATT=3.0000, SE=1.7321, p=0.1583)
results.print_summary()     # full statsmodels-style table

Documentation

For AI Agents

If you are an AI agent or LLM using this library, call diff_diff.get_llm_guide() for a concise API reference with an 8-step practitioner workflow (based on Baker et al. 2025). The workflow ensures rigorous DiD analysis - testing assumptions, running sensitivity analysis, and checking robustness, not just calling fit().

from diff_diff import get_llm_guide

get_llm_guide()                 # concise API reference
get_llm_guide("practitioner")   # 8-step workflow (Baker et al. 2025)
get_llm_guide("full")           # comprehensive documentation
get_llm_guide("autonomous")     # autonomous-agent variant

The guides are bundled in the wheel - accessible from a pip install with no network access. After estimation, call practitioner_next_steps(results) for context-aware guidance on remaining diagnostic steps.

For Data Scientists

Measuring campaign lift? Evaluating a product launch? Rolling out a policy in waves? diff-diff handles the causal inference so you can focus on the business question.

  • Which method fits my problem? - start from your business scenario (campaign in some markets, staggered rollout, survey data) and find the right estimator
  • Getting started for practitioners - end-to-end walkthrough from marketing campaign to causal estimate to stakeholder-ready result
  • Brand awareness survey tutorial - full example with complex survey design, brand funnel analysis, and staggered rollouts
  • Have BRFSS/ACS/CPS individual records? Use aggregate_survey() to roll respondent-level microdata into a geographic-period panel with inverse-variance precision weights for second-stage DiD

BusinessReport and DiagnosticReport are experimental preview classes that produce plain-English output and a structured to_dict() schema from any fitted result - wording and schema will evolve. See docs/methodology/REPORTING.md for usage and stability notes.

Practitioner Workflow (Baker et al. 2025)

For rigorous DiD analysis, follow these 8 steps. Skipping diagnostic steps produces unreliable results.

  1. Define target parameter - ATT, group-time ATT(g,t), or event-study ATT_es(e). State whether weighted or unweighted.
  2. State identification assumptions - which parallel trends variant (unconditional, conditional, PT-GT-Nev, PT-GT-NYT), no-anticipation, overlap.
  3. Test parallel trends - simple 2x2: check_parallel_trends(), equivalence_test_trends(); staggered: inspect CS event-study pre-period coefficients (generic PT tests are invalid for staggered designs). Insignificant pre-trends do NOT prove PT holds.
  4. Choose estimator - staggered adoption -> CS/SA/BJS (NOT plain TWFE); few treated units -> SDiD; factor confounding -> TROP; simple 2x2 -> DiD. Run BaconDecomposition to diagnose TWFE bias.
  5. Estimate - estimator.fit(data, ...). Always print the cluster count first and choose inference method based on the result (cluster-robust if >= 50 clusters, wild bootstrap if fewer).
  6. Sensitivity analysis - compute_honest_did(results) for bounds under PT violations (MultiPeriodDiD, CS, or dCDH), run_all_placebo_tests() for 2x2 falsification, specification comparisons for staggered designs.
  7. Heterogeneity - CS: aggregate='group'/'event_study'; SA: results.event_study_effects / to_dataframe(level='cohort'); subgroup re-estimation.
  8. Robustness - compare 2-3 estimators (CS vs SA vs BJS), report with and without covariates (shows whether conditioning drives identification), present pre-trends and sensitivity bounds.

Full guide: diff_diff.get_llm_guide("practitioner").

Estimators

  • DifferenceInDifferences - basic 2x2 DiD with robust/cluster-robust SEs, wild bootstrap, formula interface, and fixed effects
  • TwoWayFixedEffects - panel data DiD with unit and time fixed effects via within-transformation or dummies
  • MultiPeriodDiD - event study design with period-specific treatment effects for dynamic analysis
  • CallawaySantAnna - Callaway & Sant'Anna (2021) group-time ATT estimator for staggered adoption
  • ChaisemartinDHaultfoeuille - de Chaisemartin & D'Haultfœuille (2020/2022) for reversible (non-absorbing) treatments with multi-horizon event study, normalized effects, cost-benefit delta, sup-t bands, and dynamic placebos. The most general option for treatments that switch on AND off (see also LPDiD/TROP non_absorbing). Alias DCDH.
  • SunAbraham - Sun & Abraham (2021) interaction-weighted estimator for heterogeneity-robust event studies
  • ImputationDiD - Borusyak, Jaravel & Spiess (2024) imputation estimator, most efficient under homogeneous effects
  • TwoStageDiD - Gardner (2022) two-stage estimator with GMM sandwich variance
  • SpilloverDiD - Butts (2021) ring-indicator spillover-aware DiD identifying direct effect on treated + per-ring spillover on near-control units; handles non-staggered and staggered timing; supports survey-design variance under survey_design= for HC1 / CR1 (Wave E.1 Binder TSL) and Conley (Wave E.2 panel-aware stratified-Conley sandwich on per-period PSU totals; extended in Wave E.2 follow-up to conley_lag_cutoff > 0 via panel-block composition with within-PSU serial Bartlett HAC — lag>0 requires an effective PSU via explicit survey_design.psu or injected cluster=<col>); SurveyDesign.subpopulation() preserves full-design n_psu / df_survey via zero-padded scores (Wave E.3, R svyrecvar(subset()) form)
  • SyntheticDiD - Synthetic DiD combining standard DiD and synthetic control for few treated units
  • SyntheticControl - Abadie, Diamond & Hainmueller (2010) classic synthetic control for a single treated unit (donor-weight counterfactual, nested/cv/inverse-variance/custom V; in-space placebo permutation inference via in_space_placebo(), plus ADH-2015 leave_one_out() + in_time_placebo() robustness, Firpo-Possebom (2018) test-inversion confidence sets, and Chernozhukov-Wüthrich-Zhu (2021) conformal inference)
  • TripleDifference - triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility
  • ContinuousDiD - Callaway, Goodman-Bacon & Sant'Anna (2024) continuous treatment DiD with dose-response curves
  • HeterogeneousAdoptionDiD - de Chaisemartin, Ciccia, D'Haultfœuille & Knau (2026) for designs where no unit remains untreated; local-linear estimator at the dose support boundary returning Weighted Average Slope (WAS) on Design 1' (d̲ = 0 / QUG) or WAS_{d̲} on Design 1 (d̲ > 0, continuous-near-d̲ or mass-point), with a multi-period event-study extension (last-treatment cohort, pointwise CIs). Panel-only in this release - repeated cross-sections rejected by the validator. Alias HAD.
  • RegressionDiscontinuity - Calonico, Cattaneo & Titiunik (2014) sharp, fuzzy, AND covariate-adjusted regression discontinuity with robust bias-corrected inference and rdrobust-parity bandwidth selection (all 10 selectors, mass-point handling; fuzzy via treatment_col= with a first-stage block and weak-identification warning; covariates via covariates= - CCFT 2019, same estimand, covariate-aware bandwidths). Canonical att is the bias-corrected estimate with a coherent robust CI (rdrobust's printed headline is att_conventional). Alias RDD.
  • StackedDiD - Wing, Freedman & Hollingsworth (2024) stacked DiD with Q-weights and sub-experiments; optional covariate balancing (Ustyuzhanin 2026)
  • EfficientDiD - Chen, Sant'Anna & Xie (2025) efficient DiD with optimal weighting for tighter SEs
  • TROP - Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
  • StaggeredTripleDifference - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
  • WooldridgeDiD - Wooldridge (2023, 2025) ETWFE: saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias ETWFE.
  • LPDiD - Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting), variance- or equally-weighted ATT, for absorbing or non-absorbing (reversible) treatment
  • ChangesInChanges - Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: full counterfactual distribution and quantile treatment effects via CDF transformation, plus the QDiD comparison estimator; bootstrap inference; R qte parity. Alias CiC
  • BaconDecomposition - Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings

Diagnostics & Sensitivity

  • Parallel Trends Testing - simple and Wasserstein-robust parallel trends tests, equivalence testing (TOST)
  • Placebo Tests - placebo timing, group, permutation, leave-one-out
  • Honest DiD - Rambachan & Roth (2023) sensitivity analysis: robust CI under PT violations, breakdown values
  • Pre-Trends Power Analysis - Roth (2022) minimum detectable violation and power curves
  • Power Analysis - analytical and simulation-based MDE, sample size, power curves for study design
  • Conley spatial HAC SE (vcov_type="conley") on cross-sectional LinearRegression / compute_robust_vcov plus panel DifferenceInDifferences / MultiPeriodDiD / TwoWayFixedEffects (with conley_lag_cutoff for within-unit Bartlett temporal HAC) - Conley (1999) spatial-correlation-aware SEs with parity vs R conleyreg on cross-sectional + panel fixtures, optional combined spatial + cluster product kernel via explicit cluster=, auto-activating sparse k-d-tree fast path for n > 5_000

Survey Support

Most estimators accept an optional survey_design parameter (or survey= / weights= for HeterogeneousAdoptionDiD) for design-based variance estimation. Coverage and supported weight types vary by estimator - see the Survey Design Support compatibility matrix for the per-estimator support table.

  • Design elements available across the supported set: strata, PSU, FPC, lonely PSU handling, nest. Weight types vary by estimator: some surfaces (e.g. CallawaySantAnna, StackedDiD, the HAD continuous path) accept pweight only; others accept pweight / fweight / aweight.
  • Variance methods: Taylor Series Linearization (TSL via Binder 1983), replicate weights (BRR / Fay / JK1 / JKn / SDR), survey-aware bootstrap
  • Diagnostics: DEFF per coefficient, effective n, subpopulation analysis, weight trimming, CV on estimates
  • Repeated cross-sections: CallawaySantAnna(panel=False) for BRFSS, ACS, CPS
  • Weight calibration / raking: upstream by design - pair with Meta's balance package, whose balance.interop.diff_diff adapter hands raked samples straight to diff-diff; see the composition-drift tutorial

No other Python or R DiD package offers design-based variance estimation for modern heterogeneity-robust estimators.

Requirements

  • Python 3.9 - 3.14
  • numpy >= 1.20
  • pandas >= 1.3
  • scipy >= 1.10

Development

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

# Run tests
pytest

# Format code
black diff_diff tests
ruff check diff_diff tests

References

This library implements methods from a wide body of econometric and causal-inference research. See the full bibliography on Read the Docs for citations spanning DiD foundations, modern staggered estimators, sensitivity analysis, and synthetic controls.

Citing diff-diff

If you use diff-diff in your research, please cite it:

@software{diff_diff,
  title = {diff-diff: Difference-in-Differences Causal Inference for Python},
  author = {Gerber, Isaac},
  year = {2026},
  url = {https://github.com/igerber/diff-diff},
  doi = {10.5281/zenodo.19646175},
  license = {MIT},
}

The DOI above is the Zenodo concept DOI - it always resolves to the latest release. To cite a specific version, look up its versioned DOI on the Zenodo project page.

See CITATION.cff for the full citation metadata.

Note on authorship: academic citation (CITATION.cff, the BibTeX above) lists individual authors with ORCIDs per scholarly convention. Package metadata surfaces (pyproject.toml, Sphinx docs) list "diff-diff contributors" to acknowledge the collective - see CONTRIBUTORS.md for the full list.

License

MIT License

Project links

Data verified by PyPI on Jul 18, 2026
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  • Released:
Newer release available (3.9.0)

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Credits

Author: diff-diff contributors

GitHub Statistics

Data verified by PyPI on Jul 18, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

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MIT
View SPDX License List

Requires

Python <3.15, >=3.9

Provides Extra

dev docs plotly

Tags

causal-inference difference-in-differences econometrics statistics treatment-effects event-study staggered-adoption parallel-trends synthetic-control panel-data did twfe callaway-santanna honest-did sensitivity-analysis

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