diff --git a/ROADMAP.md b/ROADMAP.md index 2227a7fa..07b9b9e9 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -8,9 +8,9 @@ For past changes and release history, see [CHANGELOG.md](CHANGELOG.md). ## Current Status -diff-diff v2.4.1 is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis: +diff-diff v2.6.0 is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis: -- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024) +- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024), Continuous DiD (Callaway, Goodman-Bacon & Sant'Anna 2024) - **Valid inference**: Robust SEs, cluster SEs, wild bootstrap, multiplier bootstrap, placebo-based variance - **Assumption diagnostics**: Parallel trends tests, placebo tests, Goodman-Bacon decomposition - **Sensitivity analysis**: Honest DiD (Rambachan-Roth), Pre-trends power analysis (Roth 2022) @@ -20,35 +20,15 @@ diff-diff v2.4.1 is a **production-ready** DiD library with feature parity with --- -## Near-Term Enhancements (v2.5) - -High-value additions building on our existing foundation. - -### ~~Stacked Difference-in-Differences~~ (Implemented in v2.5) - -Implemented as `StackedDiD`. See `diff_diff/stacked_did.py`. +## Near-Term Enhancements (v2.7) ### Staggered Triple Difference (DDD) -Extend the existing `TripleDifference` estimator to handle staggered adoption settings. The current implementation handles 2-period DDD; this extends to multi-period designs. +Extend the existing `TripleDifference` estimator to handle staggered adoption settings. -**Multi-period/Staggered Support:** - Group-time ATT(g,t) for DDD designs with variation in treatment timing -- Handle settings where groups adopt at different times -- Multiple comparison groups (never-treated, not-yet-treated in either dimension) -- `StaggeredTripleDifference` class or extended `TripleDifference` with `first_treat` parameter - -**Event Study Aggregation:** -- Dynamic treatment effects over time (event study coefficients) -- Pre-treatment placebo effects for parallel trends assessment -- `aggregate='event_study'` parameter like `CallawaySantAnna` -- Integration with `plot_event_study()` visualization - -**Multiplier Bootstrap Inference:** +- Event study aggregation and pre-treatment placebo effects - Multiplier bootstrap for valid inference in staggered settings -- Rademacher, Mammen, and Webb weight options (matching existing estimators) -- `n_bootstrap` parameter and `DDDBootstrapResults` class -- Clustered bootstrap for panel data **Reference**: [Ortiz-Villavicencio & Sant'Anna (2025)](https://arxiv.org/abs/2505.09942). *Working Paper*. R package: `triplediff`. @@ -60,22 +40,13 @@ Extend the existing `TripleDifference` estimator to handle staggered adoption se --- -## Medium-Term Enhancements (v2.5+) - -Extending diff-diff to handle more complex settings. +## Medium-Term Enhancements -### Continuous Treatment DiD +### Efficient DiD Estimators -Many treatments have dose/intensity rather than binary on/off. Active research area with recent breakthroughs. +Semiparametrically efficient versions of existing DiD/event-study estimators with 40%+ precision gains over current methods. -- Treatment effect on treated (ATT) parameters under generalized parallel trends -- Dose-response curves and marginal effects -- Handle settings where "dose" varies across units and time -- Event studies with continuous treatments - -**References**: -- [Callaway, Goodman-Bacon & Sant'Anna (2024)](https://arxiv.org/abs/2107.02637). *NBER Working Paper*. -- [de Chaisemartin, D'Haultfœuille & Vazquez-Bare (2024)](https://arxiv.org/abs/2402.05432). *AEA Papers and Proceedings*. +**Reference**: [Chen, Sant'Anna & Xie (2025)](https://arxiv.org/abs/2506.17729). *Working Paper*. ### de Chaisemartin-D'Haultfœuille Estimator @@ -84,7 +55,6 @@ Handles treatment that switches on and off (reversible treatments), unlike most - Allows units to move into and out of treatment - Time-varying, heterogeneous treatment effects - Comparison with never-switchers or flexible control groups -- Different assumptions than CS/SA—useful for different settings **Reference**: [de Chaisemartin & D'Haultfœuille (2020, 2024)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3980758). *American Economic Review*. @@ -95,7 +65,6 @@ Implements local projections for dynamic treatment effects. Doesn't require spec - Flexible impulse response estimation - Robust to misspecification of dynamics - Natural handling of anticipation effects -- Growing use in macroeconomics and policy evaluation **Reference**: Dube, Girardi, Jordà, and Taylor (2023). @@ -105,30 +74,16 @@ For outcomes where linear models are inappropriate (binary, count, bounded). - Logit/probit DiD for binary outcomes - Poisson DiD for count outcomes -- Flexible strategies for staggered designs with nonlinear models - Proper handling of incidence rate ratios and odds ratios **Reference**: [Wooldridge (2023)](https://academic.oup.com/ectj/article/26/3/C31/7250479). *The Econometrics Journal*. -### Doubly Robust DiD + Synthetic Control - -Unified framework combining DiD and synthetic control with doubly robust identification—valid under *either* parallel trends or synthetic control assumptions. - -- ATT identified under parallel trends OR group-level SC condition -- Semiparametric estimation framework -- Multiplier bootstrap for valid inference under either assumption -- Strengthens credibility by avoiding the DiD vs. SC trade-off - -**Reference**: [Sun, Xie & Zhang (2025)](https://arxiv.org/abs/2503.11375). *Working Paper*. - ### Causal Duration Analysis with DiD Extends DiD to duration/survival outcomes where standard methods fail (hazard rates, time-to-event). - Duration analogue of parallel trends on hazard rates - Avoids distributional assumptions and hazard function specification -- Visual and formal pre-trends assessment for duration data -- Handles absorbing states approaching probability bounds **Reference**: [Deaner & Ku (2025)](https://www.aeaweb.org/conference/2025/program/paper/k77Kh8iS). *AEA Conference Paper*. @@ -138,16 +93,28 @@ Extends DiD to duration/survival outcomes where standard methods fail (hazard ra Frontier methods requiring more research investment. -### Matrix Completion Methods +### DiD with Interference / Spillovers -Unified framework encompassing synthetic control and regression approaches. Moves seamlessly between cross-sectional and time-series patterns. +Standard DiD assumes SUTVA; spatial/network spillovers violate this. Two-stage imputation approach estimates treatment AND spillover effects under staggered timing. -- Nuclear norm regularization for low-rank structure -- Handles missing data patterns common in panel settings -- Bridges synthetic control (few units, many periods) and regression (many units, few periods) -- Confidence intervals via debiasing +**Reference**: [Butts (2024)](https://arxiv.org/abs/2105.03737). *Working Paper*. -**Reference**: [Athey et al. (2021)](https://arxiv.org/abs/1710.10251). *Journal of the American Statistical Association*. +### Quantile/Distributional DiD + +Recover the full counterfactual distribution and quantile treatment effects (QTT), not just mean ATT. Goes beyond "what's the average effect" to "who gains, who loses." + +- Changes-in-Changes (CiC) identification strategy +- QTT(τ) at user-specified quantiles +- Full counterfactual distribution function +- Two-period foundation, then staggered extension + +**Reference**: [Athey & Imbens (2006)](https://onlinelibrary.wiley.com/doi/10.1111/j.1468-0262.2006.00668.x). *Econometrica*. + +### CATT Meta-Learner for Heterogeneous Effects + +ML-powered conditional ATT — discover who benefits most from treatment using doubly robust meta-learner. + +**Reference**: [Lan, Chang, Dillon & Syrgkanis (2025)](https://arxiv.org/abs/2502.04699). *Working Paper*. ### Causal Forests for DiD @@ -155,21 +122,27 @@ Machine learning methods for discovering heterogeneous treatment effects in DiD - Estimate treatment effect heterogeneity across covariates - Data-driven subgroup discovery -- Combine with DiD identification for observational data - Honest confidence intervals for discovered heterogeneity **References**: - [Kattenberg, Scheer & Thiel (2023)](https://ideas.repec.org/p/cpb/discus/452.html). *CPB Discussion Paper*. - Athey & Wager (2019). *Annals of Statistics*. +### Matrix Completion Methods + +Unified framework encompassing synthetic control and regression approaches. + +- Nuclear norm regularization for low-rank structure +- Bridges synthetic control (few units, many periods) and regression (many units, few periods) + +**Reference**: [Athey et al. (2021)](https://arxiv.org/abs/1710.10251). *Journal of the American Statistical Association*. + ### Double/Debiased ML for DiD For high-dimensional settings with many potential confounders. - ML for nuisance parameter estimation (propensity, outcome models) - Cross-fitting for valid inference -- Handles many covariates without overfitting concerns -- Doubly-robust estimation with ML flexibility **Reference**: Chernozhukov et al. (2018). *The Econometrics Journal*. @@ -183,10 +156,6 @@ For high-dimensional settings with many potential confounders. ## Infrastructure Improvements -Ongoing maintenance and developer experience. - -### Documentation - - Video tutorials and worked examples ---