Skip to content

ROADMAP.md

Latest commit

 

History

History
169 lines (100 loc) · 6.77 KB

ROADMAP.md

File metadata and controls

169 lines (100 loc) · 6.77 KB

diff-diff Roadmap

This document outlines the feature roadmap for diff-diff, prioritized by practitioner value and academic credibility.

For past changes and release history, see CHANGELOG.md.

Current Status

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), 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)
  • Study design: Power analysis tools
  • Data utilities: Real-world datasets (Card-Krueger, Castle Doctrine, Divorce Laws, MPDTA), DGP functions for all supported designs
  • Performance: Optional Rust backend for accelerated computation; faster than R at scale (see CHANGELOG.md for benchmarks)

Near-Term Enhancements (v2.7)

Staggered Triple Difference (DDD)

Extend the existing TripleDifference estimator to handle staggered adoption settings.

  • Group-time ATT(g,t) for DDD designs with variation in treatment timing
  • Event study aggregation and pre-treatment placebo effects
  • Multiplier bootstrap for valid inference in staggered settings

Reference: Ortiz-Villavicencio & Sant'Anna (2025). Working Paper. R package: triplediff.

Enhanced Visualization

  • Synthetic control weight visualization (bar chart of unit weights)
  • Treatment adoption "staircase" plot for staggered designs
  • Interactive plots with plotly backend option

Medium-Term Enhancements

Efficient DiD Estimators

Semiparametrically efficient versions of existing DiD/event-study estimators with 40%+ precision gains over current methods.

Reference: Chen, Sant'Anna & Xie (2025). Working Paper.

de Chaisemartin-D'Haultfœuille Estimator

Handles treatment that switches on and off (reversible treatments), unlike most other methods.

  • Allows units to move into and out of treatment
  • Time-varying, heterogeneous treatment effects
  • Comparison with never-switchers or flexible control groups

Reference: de Chaisemartin & D'Haultfœuille (2020, 2024). American Economic Review.

Local Projections DiD

Implements local projections for dynamic treatment effects. Doesn't require specifying full dynamic structure.

  • Flexible impulse response estimation
  • Robust to misspecification of dynamics
  • Natural handling of anticipation effects

Reference: Dube, Girardi, Jordà, and Taylor (2023).

Nonlinear DiD

For outcomes where linear models are inappropriate (binary, count, bounded).

  • Logit/probit DiD for binary outcomes
  • Poisson DiD for count outcomes
  • Proper handling of incidence rate ratios and odds ratios

Reference: Wooldridge (2023). The Econometrics Journal.

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

Reference: Deaner & Ku (2025). AEA Conference Paper.

Long-Term Research Directions (v3.0+)

Frontier methods requiring more research investment.

DiD with Interference / Spillovers

Standard DiD assumes SUTVA; spatial/network spillovers violate this. Two-stage imputation approach estimates treatment AND spillover effects under staggered timing.

Reference: Butts (2024). Working Paper.

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). 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). Working Paper.

Causal Forests for DiD

Machine learning methods for discovering heterogeneous treatment effects in DiD settings.

  • Estimate treatment effect heterogeneity across covariates
  • Data-driven subgroup discovery
  • Honest confidence intervals for discovered heterogeneity

References:

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). 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

Reference: Chernozhukov et al. (2018). The Econometrics Journal.

Alternative Inference Methods

  • Randomization inference: Exact p-values for small samples
  • Bayesian DiD: Priors on parallel trends violations
  • Conformal inference: Prediction intervals with finite-sample guarantees

Infrastructure Improvements

  • Video tutorials and worked examples

Contributing

Interested in contributing? Features in the "Near-Term" and "Medium-Term" sections are good candidates. See the GitHub repository for open issues.

Key references for implementation: