From 4328197326290b0043879bcdcc8c23c9d45f5a01 Mon Sep 17 00:00:00 2001
From: Claude <noreply@anthropic.com>
Date: Mon, 5 Jan 2026 11:38:12 +0000
Subject: [PATCH 1/2] Update roadmap and TODO for v1.0.2 with future focus
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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- Remove completed items tracking (now in CHANGELOG.md)
- Reorganize roadmap into Near-Term, Medium-Term, Long-Term sections
- Add new research-backed future enhancements:
  - Continuous Treatment DiD (Callaway, Goodman-Bacon, Sant'Anna 2024)
  - de Chaisemartin-D'Haultfœuille (reversible treatments)
  - Nonlinear DiD (Wooldridge 2023)
  - Matrix Completion Methods (Athey et al.)
  - Causal Forests for HTE in DiD settings
- Clean up TODO.md by removing completed items
- Focus on actionable technical debt and improvements
---
 ROADMAP.md | 232 +++++++++++++++++++++++++++--------------------------
 TODO.md    | 118 ++++++++-------------------
 2 files changed, 151 insertions(+), 199 deletions(-)

diff --git a/ROADMAP.md b/ROADMAP.md
index a4406e8..f0a1274 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -2,190 +2,192 @@
 
 This document outlines the feature roadmap for diff-diff, prioritized by practitioner value and academic credibility.
 
-## What Makes a Credible 1.0?
+For past changes and release history, see [CHANGELOG.md](CHANGELOG.md).
 
-A production-ready DiD library needs:
-
-1. ✅ **Core estimators** - Basic DiD, TWFE, MultiPeriod, Staggered (Callaway-Sant'Anna), Synthetic DiD
-2. ✅ **Valid inference** - Robust SEs, cluster SEs, wild bootstrap for few clusters
-3. ✅ **Assumption diagnostics** - Parallel trends tests, placebo tests
-4. ✅ **Sensitivity analysis** - What if parallel trends is violated? (Rambachan-Roth)
-5. ✅ **Conditional parallel trends** - Covariate adjustment for staggered DiD
-6. ✅ **Documentation** - API reference site for discoverability
+---
 
-**All 1.0 blockers are complete.** diff-diff has feature parity with R's `did` + `HonestDiD` ecosystem for core DiD analysis.
+## Current Status (v1.0.2)
 
----
+diff-diff is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` ecosystem for core DiD analysis:
 
-## Status Overview
-
-| Feature | Status | Priority | Why It Matters |
-|---------|--------|----------|----------------|
-| Honest DiD (Rambachan-Roth) | ✅ Done | — | Reviewers expect sensitivity analysis |
-| CallawaySantAnna Covariates | ✅ Done | — | Conditional PT often required in practice |
-| API Documentation Site | ✅ Done | — | Credibility and discoverability |
-| Goodman-Bacon Decomposition | ✅ Done | — | Explains when TWFE fails |
-| Power Analysis | ✅ Done | — | Study design tool |
-| CallawaySantAnna Bootstrap | ✅ Done | — | Valid inference with few clusters |
-| Sun-Abraham Estimator | Not Started | Post-1.0 | Alternative to CS, some prefer it |
-| Gardner's did2s | Not Started | Post-1.0 | Two-stage approach, available in pyfixest |
-| Local Projections DiD | Not Started | Post-1.0 | Dynamic effects (Dube et al. 2023) |
-| Borusyak-Jaravel-Spiess | Not Started | Post-1.0 | More efficient under homogeneous effects |
-| Double/Debiased ML | Not Started | Post-1.0 | High-dimensional covariates |
+- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Synthetic DiD
+- **Valid inference**: Robust SEs, cluster SEs, wild bootstrap, multiplier bootstrap
+- **Assumption diagnostics**: Parallel trends tests, placebo tests, Goodman-Bacon decomposition
+- **Sensitivity analysis**: Honest DiD (Rambachan-Roth)
+- **Study design**: Power analysis tools
 
 ---
 
-## 1.0 Target Features
+## Near-Term Enhancements (v1.1–v1.2)
 
-These would strengthen the 1.0 release but aren't strictly blocking.
+High-value additions building on our existing foundation.
 
-### ✅ Goodman-Bacon Decomposition (Done)
+### Sun-Abraham Estimator
 
-Helps users understand *why* TWFE can be biased with staggered adoption. Shows weights on "forbidden comparisons" (already-treated as controls). Essential diagnostic before deciding whether to use Callaway-Sant'Anna.
+Interaction-weighted estimator providing an alternative to Callaway-Sant'Anna. Many practitioners run both as a robustness check.
 
-- ✅ Decompose TWFE into 2x2 comparisons
-- ✅ Show weights by comparison type (clean vs. forbidden)
-- ✅ Visualization of decomposition (scatter and bar charts)
-- ✅ Integration with `TwoWayFixedEffects.decompose()` method
-- ✅ Automatic warning when TWFE detects staggered treatment timing
+- Event-study coefficients via saturated regression with cohort-time interactions
+- Different weighting scheme than CS; can give different results under heterogeneous effects
+- Useful robustness check when CS and SA agree
 
-**Reference**: Goodman-Bacon (2021). *Journal of Econometrics*.
+**Reference**: Sun & Abraham (2021). *Journal of Econometrics*.
 
-### ✅ Power Analysis Tools (Done)
+### Borusyak-Jaravel-Spiess Imputation Estimator
 
-Practitioners need to know "how many units/periods do I need to detect an effect of size X?" Now available in diff-diff.
+More efficient than Callaway-Sant'Anna when treatment effects are homogeneous across groups/time. Uses imputation rather than aggregation.
 
-- ✅ Minimum detectable effect given sample size
-- ✅ Required sample size for target power
-- ✅ Simulation-based power for any estimator (including staggered designs)
-- ✅ Visualization of power curves
-- ✅ Panel data considerations (ICC, multiple periods)
+- Imputes untreated potential outcomes using pre-treatment data
+- More efficient under homogeneous effects assumption
+- Can handle unbalanced panels more naturally
 
-**References**: Bloom (1995); Burlig, Preonas, & Woerman (2020).
+**Reference**: Borusyak, Jaravel, and Spiess (2024). *Review of Economic Studies*.
 
-### ✅ CallawaySantAnna Bootstrap Inference (Done)
+### Gardner's Two-Stage DiD (did2s)
 
-With few clusters or groups, analytical SEs may be unreliable. Multiplier bootstrap provides valid inference following the R `did` package approach.
+Two-stage approach gaining traction in applied work. First residualizes outcomes, then estimates effects.
 
-- ✅ Multiplier bootstrap at unit level with influence function perturbation
-- ✅ Aggregate bootstrap samples for overall ATT, event study, and group effects
-- ✅ Rademacher, Mammen, and Webb weight distributions
-- ✅ Percentile confidence intervals and bootstrap p-values
+- Stage 1: Estimate unit and time FEs using only untreated observations
+- Stage 2: Regress residualized outcomes on treatment indicators
+- Clean separation of identification and estimation
 
-**Reference**: Callaway & Sant'Anna (2021). *Journal of Econometrics*.
+**Reference**: Gardner (2022). *Working Paper*.
 
 ### Enhanced Visualization
 
 - Synthetic control weight visualization (bar chart of unit weights)
-- ✅ Bacon decomposition visualization (scatter and bar charts)
-- Treatment adoption "staircase" plot
+- Treatment adoption "staircase" plot for staggered designs
+- Interactive plots with plotly backend option
 
 ---
 
-## Post-1.0 Features
+## Medium-Term Enhancements (v1.3+)
 
-These are valuable but can wait for future versions.
+Extending diff-diff to handle more complex settings.
 
-### Sun-Abraham Estimator
+### Continuous Treatment DiD
 
-Alternative to Callaway-Sant'Anna using interaction-weighted approach. Some practitioners prefer it; provides a robustness check.
+Many treatments have dose/intensity rather than binary on/off. Active research area with recent breakthroughs.
 
-**Reference**: Sun & Abraham (2021). *Journal of Econometrics*.
+- 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
 
-### Gardner's Two-Stage DiD (did2s)
+**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*.
+
+### de Chaisemartin-D'Haultfœuille Estimator
+
+Handles treatment that switches on and off (reversible treatments), unlike most other methods.
 
-Two-stage approach to staggered DiD that first residualizes outcomes using untreated observations, then estimates treatment effects. Available in pyfixest (Python) and did2s (R).
+- 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**: Gardner (2022). *Two-stage differences in differences*.
+**Reference**: [de Chaisemartin & D'Haultfœuille (2020, 2024)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3980758). *American Economic Review*.
 
 ### Local Projections DiD
 
-Implements local projections for dynamic treatment effects. Flexible approach that doesn't require specifying the full dynamic structure. Gaining traction in applied work.
+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
+- Growing use in macroeconomics and policy evaluation
 
 **Reference**: Dube, Girardi, Jordà, and Taylor (2023).
 
-### Borusyak-Jaravel-Spiess Imputation Estimator
+### Nonlinear DiD
 
-More efficient than Callaway-Sant'Anna when parallel trends holds across all periods. Uses imputation approach.
+For outcomes where linear models are inappropriate (binary, count, bounded).
 
-**Reference**: Borusyak, Jaravel, and Spiess (2024).
+- 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
 
-### Double/Debiased ML for DiD
+**Reference**: [Wooldridge (2023)](https://academic.oup.com/ectj/article/26/3/C31/7250479). *The Econometrics Journal*.
 
-For high-dimensional settings with many covariates. Uses ML for nuisance parameter estimation with cross-fitting.
+---
 
-**Reference**: Chernozhukov et al. (2018), Chang (2020).
+## Long-Term Research Directions (v2.0+)
 
-### Alternative Inference Methods
+Frontier methods requiring more research investment.
 
-- Randomization inference for small samples
-- Bayesian DiD with prior on parallel trends
-- Conformal inference for prediction intervals
+### Matrix Completion Methods
 
----
+Unified framework encompassing synthetic control and regression approaches. Moves seamlessly between cross-sectional and time-series patterns.
 
-## Release History
+- 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
 
-### v0.9.0 (Current)
+**Reference**: [Athey et al. (2021)](https://arxiv.org/abs/1710.10251). *Journal of the American Statistical Association*.
 
-- ✅ Callaway-Sant'Anna multiplier bootstrap inference
-- ✅ Rademacher, Mammen, and Webb weight distributions
-- ✅ Bootstrap SEs, CIs, and p-values for all aggregations (overall ATT, event study, group effects)
-- ✅ `CSBootstrapResults` dataclass for bootstrap results
+### Causal Forests for DiD
 
-### v0.8.0
+Machine learning methods for discovering heterogeneous treatment effects in DiD settings.
 
-- ✅ Power analysis tools (`PowerAnalysis`, `simulate_power`)
-- ✅ MDE, sample size, and power calculations
-- ✅ Simulation-based power for any DiD estimator
-- ✅ Power curve visualization (`plot_power_curve`)
-- ✅ Panel data support with ICC adjustment
+- Estimate treatment effect heterogeneity across covariates
+- Data-driven subgroup discovery
+- Combine with DiD identification for observational data
+- Honest confidence intervals for discovered heterogeneity
 
-### v0.7.0
+**References**:
+- [Kattenberg, Scheer & Thiel (2023)](https://ideas.repec.org/p/cpb/discus/452.html). *CPB Discussion Paper*.
+- Athey & Wager (2019). *Annals of Statistics*.
 
-- ✅ Goodman-Bacon decomposition for TWFE diagnostics
-- ✅ `plot_bacon()` visualization (scatter and bar charts)
-- ✅ `TwoWayFixedEffects.decompose()` integration
-- ✅ Automatic staggered treatment warning in TWFE
+### 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
 
-### v0.6.0
+**Reference**: Chernozhukov et al. (2018). *The Econometrics Journal*.
 
-- ✅ **All 1.0 Blockers Complete**
-- ✅ Honest DiD sensitivity analysis (Rambachan & Roth 2023)
-- ✅ CallawaySantAnna covariate adjustment (DR, IPW, Reg)
-- ✅ API documentation site with Sphinx
+### Alternative Inference Methods
 
-### v0.5.0
+- **Randomization inference**: Exact p-values for small samples
+- **Bayesian DiD**: Priors on parallel trends violations
+- **Conformal inference**: Prediction intervals with finite-sample guarantees
 
-- Wild cluster bootstrap (Rademacher, Webb, Mammen weights)
-- Placebo tests module
-- Tutorial notebooks
+---
 
-### v0.4.0
+## Infrastructure Improvements
 
-- Callaway-Sant'Anna estimator for staggered DiD
-- Event study and group effects visualization
-- Parallel trends testing utilities
+Ongoing maintenance and developer experience.
 
-### v0.3.0
+### Performance
 
-- Synthetic Difference-in-Differences
-- Multi-period DiD with event study
-- Data preparation utilities
+- JIT compilation for bootstrap loops (numba)
+- Parallel bootstrap iterations
+- Sparse matrix handling for large fixed effects
+- Memory-efficient estimation for large panels
 
-### v0.2.0
+### Code Quality
 
-- Two-Way Fixed Effects estimator
-- Fixed effects support (absorb parameter)
-- Cluster-robust standard errors
-- Formula interface
+- Extract shared within-transformation logic to utils
+- Consolidate linear regression helpers
+- Consider splitting `staggered.py` (1800+ lines)
 
-### v0.1.0
+### Documentation
 
-- Initial release with basic DiD estimator
+- Real-world data examples (beyond synthetic)
+- Performance benchmarks vs. R packages
+- Video tutorials and worked examples
 
 ---
 
 ## Contributing
 
-Interested in contributing? See the [GitHub repository](https://github.com/igerber/diff-diff) for open issues. Features marked "Not Started" are good candidates for contributions.
+Interested in contributing? Features in the "Near-Term" and "Medium-Term" sections are good candidates. See the [GitHub repository](https://github.com/igerber/diff-diff) for open issues.
+
+Key references for implementation:
+- [Roth et al. (2023)](https://www.sciencedirect.com/science/article/abs/pii/S0304407623001318). "What's Trending in Difference-in-Differences?" *Journal of Econometrics*.
+- [Baker et al. (2025)](https://arxiv.org/pdf/2503.13323). "Difference-in-Differences Designs: A Practitioner's Guide."
diff --git a/TODO.md b/TODO.md
index b4b2ae1..31eab5f 100644
--- a/TODO.md
+++ b/TODO.md
@@ -6,97 +6,46 @@ For the public feature roadmap, see [ROADMAP.md](ROADMAP.md).
 
 ---
 
-## Priority Items for 1.0.1
-
-### Linter/Type Errors (Blocking) - COMPLETED
-
-| Issue | Location | Status |
-|-------|----------|--------|
-| ~~Unused import `Union`~~ | `power.py:25` | Fixed |
-| ~~Unsorted imports~~ | `staggered.py:8` | Fixed |
-| ~~10 mypy errors - Optional type handling~~ | `staggered.py:843-1631` | Fixed |
-
-### Quick Wins - COMPLETED
-
-- [x] Fix ruff errors (2 auto-fixable)
-- [x] Fix mypy errors in staggered.py (Optional dict access needs guards)
-- [x] Remove duplicate `_get_significance_stars()` from `diagnostics.py` (now imports from `results.py`)
-
----
-
 ## Known Limitations
 
+Current limitations that may affect users:
+
 | Issue | Location | Priority | Notes |
 |-------|----------|----------|-------|
 | MultiPeriodDiD wild bootstrap not supported | `estimators.py:1068-1074` | Low | Edge case |
 | `predict()` raises NotImplementedError | `estimators.py:532-554` | Low | Rarely needed |
-| SyntheticDiD bootstrap can fail silently | `estimators.py:1580-1654` | Medium | Needs error handling |
+| SyntheticDiD bootstrap can fail silently | `estimators.py:1580-1654` | Medium | Needs better error handling |
 | Diagnostics module error handling | `diagnostics.py:782-885` | Medium | Improve robustness |
 
 ---
 
-## Code Quality Issues
-
-### Bare Exception Handling - COMPLETED
-
-~~Replace broad `except Exception` with specific exceptions:~~
-
-| Location | Status |
-|----------|--------|
-| ~~`diagnostics.py:624`~~ | Fixed - catches `ValueError`, `KeyError`, `LinAlgError` |
-| ~~`diagnostics.py:735`~~ | Fixed - catches `ValueError`, `KeyError`, `LinAlgError` |
-| ~~`honest_did.py:807`~~ | Fixed - catches `ValueError`, `TypeError` |
-| ~~`honest_did.py:822`~~ | Fixed - catches `ValueError`, `TypeError` |
+## Code Quality
 
 ### Code Duplication
 
-| Duplicate Code | Locations | Status |
-|---------------|-----------|--------|
-| ~~`_get_significance_stars()`~~ | `results.py:183`, ~~`diagnostics.py`~~ | Fixed in 1.0.1 |
-| Wild bootstrap inference block | `estimators.py:278-296`, `estimators.py:725-748` | Future: extract to shared method |
-| Within-transformation logic | `estimators.py:217-232`, `estimators.py:787-833`, `bacon.py:567-642` | Future: extract to utils.py |
-| Linear regression helper | `staggered.py:205-240`, `estimators.py:366-408` | Future: consider consolidation |
-
-### API Inconsistencies - PARTIALLY ADDRESSED
+Consolidation opportunities for cleaner maintenance:
 
-**Bootstrap parameter naming:**
-| Estimator | Parameter | Status |
-|-----------|-----------|--------|
-| DifferenceInDifferences | `bootstrap_weights` | OK |
-| CallawaySantAnna | `bootstrap_weights` | Fixed in 1.0.1 (deprecated `bootstrap_weight_type`) |
-| TwoWayFixedEffects | `bootstrap_weights` | OK |
+| Duplicate Code | Locations | Notes |
+|---------------|-----------|-------|
+| Wild bootstrap inference block | `estimators.py:278-296`, `estimators.py:725-748` | Extract to shared method |
+| Within-transformation logic | `estimators.py:217-232`, `estimators.py:787-833`, `bacon.py:567-642` | Extract to utils.py |
+| Linear regression helper | `staggered.py:205-240`, `estimators.py:366-408` | Consider consolidation |
 
-**Cluster variable defaults:**
-- ~~`TwoWayFixedEffects` silently defaults cluster to `unit` at runtime~~ - Documented in 1.0.1
-
----
+### Large Module Files
 
-## Large Module Files
+Target: < 1000 lines per module for maintainability.
 
-Current line counts (target: < 1000 lines per module):
-
-| File | Lines | Status |
+| File | Lines | Action |
 |------|-------|--------|
-| `staggered.py` | 1822 | Consider splitting |
-| `estimators.py` | ~975 | OK (refactored) |
-| `twfe.py` | ~355 | OK (new) |
-| `synthetic_did.py` | ~540 | OK (new) |
+| `staggered.py` | 1822 | Consider splitting to `staggered_bootstrap.py` |
 | `honest_did.py` | 1491 | Acceptable |
+| `visualization.py` | 1388 | Acceptable |
 | `utils.py` | 1350 | Acceptable |
 | `power.py` | 1350 | Acceptable |
 | `prep.py` | 1338 | Acceptable |
-| `visualization.py` | 1388 | Acceptable |
 | `bacon.py` | 1027 | OK |
 
-**Completed splits:**
-- ~~`estimators.py` → `twfe.py`, `synthetic_did.py` (keep base classes in estimators.py)~~ - Done in 1.0.2
-
-**Potential splits:**
-- `staggered.py` → `staggered_bootstrap.py` (move bootstrap logic)
-
----
-
-## Standard Error Consistency
+### Standard Error Consistency
 
 Different estimators compute SEs differently. Consider unified interface.
 
@@ -107,7 +56,7 @@ Different estimators compute SEs differently. Consider unified interface.
 | CallawaySantAnna | Simple difference-in-means SE |
 | SyntheticDiD | Bootstrap or placebo-based |
 
-**Action**: Audit and document SE computation across estimators. Consider adding `se_type` parameter for consistency.
+**Action**: Consider adding `se_type` parameter for consistency across estimators.
 
 ---
 
@@ -122,7 +71,7 @@ Edge cases needing tests:
 - [ ] CallawaySantAnna with single cohort
 - [ ] SyntheticDiD with insufficient pre-periods
 
-**Note**: 21 visualization tests are skipped when matplotlib unavailable - this is expected.
+**Note**: 21 visualization tests are skipped when matplotlib unavailable—this is expected.
 
 ---
 
@@ -135,18 +84,9 @@ Edge cases needing tests:
 
 ---
 
-## CallawaySantAnna Bootstrap Improvements
-
-Deferred improvements from code review (PR #32):
-
-- [ ] Refactor `_run_multiplier_bootstrap` into smaller helper methods for maintainability
-- [ ] Consider aligning p-value computation with R `did` package (symmetric percentile method)
-
----
-
-## Honest DiD Future Improvements
+## Honest DiD Improvements
 
-Post-1.0 enhancements for `honest_did.py`:
+Enhancements for `honest_did.py`:
 
 - [ ] Improved C-LF implementation with direct optimization instead of grid search
 - [ ] Support for CallawaySantAnnaResults (currently only MultiPeriodDiDResults)
@@ -156,19 +96,29 @@ Post-1.0 enhancements for `honest_did.py`:
 
 ---
 
-## Performance
+## CallawaySantAnna Bootstrap Improvements
+
+From code review (PR #32):
+
+- [ ] Refactor `_run_multiplier_bootstrap` into smaller helper methods
+- [ ] Consider aligning p-value computation with R `did` package (symmetric percentile method)
+
+---
+
+## Performance Optimizations
 
 No major performance issues identified. Potential future optimizations:
 
-- JIT compilation for bootstrap loops (numba)
-- Parallel bootstrap iterations
-- Sparse matrix handling for large fixed effects
+- [ ] JIT compilation for bootstrap loops (numba)
+- [ ] Parallel bootstrap iterations
+- [ ] Sparse matrix handling for large fixed effects
 
 ---
 
 ## Type Hints
 
 Missing type hints in internal functions:
+
 - `utils.py:593` - `compute_trend()` nested function
 - `staggered.py:173, 180` - Nested functions in `_logistic_regression()`
 - `prep.py:604` - `format_label()` nested function

From 33fd41f7a979111d1d64e08a3dce556fc50f7a3b Mon Sep 17 00:00:00 2001
From: Claude <noreply@anthropic.com>
Date: Mon, 5 Jan 2026 11:45:47 +0000
Subject: [PATCH 2/2] Add 2025 DiD research to roadmap

Near-Term additions:
- Triple Difference (DDD) Estimators (Ortiz-Villavicencio & Sant'Anna 2025)
- Pre-Trends Power Analysis (Roth 2022, complements Honest DiD)

Medium-Term additions:
- Doubly Robust DiD + Synthetic Control (Sun, Xie & Zhang 2025)
- Causal Duration Analysis with DiD (Deaner & Ku 2025)

These represent the latest methodological advances in the DiD literature.
---
 ROADMAP.md | 43 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/ROADMAP.md b/ROADMAP.md
index f0a1274..625f4b8 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -52,6 +52,27 @@ Two-stage approach gaining traction in applied work. First residualizes outcomes
 
 **Reference**: Gardner (2022). *Working Paper*.
 
+### Triple Difference (DDD) Estimators
+
+Extends DiD to settings requiring a third differencing dimension. Common DDD implementations are invalid when covariates are needed for identification.
+
+- Regression adjustment, IPW, and doubly robust DDD estimators
+- Staggered adoption support with multiple comparison groups
+- Proper covariate integration (naive "two DiD difference" approaches fail)
+- Bias reduction and precision gains over standard approaches
+
+**Reference**: [Ortiz-Villavicencio & Sant'Anna (2025)](https://arxiv.org/abs/2505.09942). *Working Paper*. R package: `triplediff`.
+
+### Pre-Trends Power Analysis
+
+Assess whether pre-trends tests have adequate power to detect meaningful parallel trends violations. Complements our Honest DiD implementation.
+
+- Minimum detectable violation size for pre-trends tests
+- Visualization of power against various violation magnitudes
+- Integration with existing parallel trends diagnostics
+
+**Reference**: [Roth (2022)](https://www.aeaweb.org/articles?id=10.1257/aeri.20210236). *AER: Insights*. R package: `pretrends`.
+
 ### Enhanced Visualization
 
 - Synthetic control weight visualization (bar chart of unit weights)
@@ -110,6 +131,28 @@ For outcomes where linear models are inappropriate (binary, count, bounded).
 
 **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*.
+
 ---
 
 ## Long-Term Research Directions (v2.0+)