Document deferred DDD features in ROADMAP.md

Add detailed roadmap for Staggered Triple Difference (v1.4):
- Multi-period/staggered adoption support
- Event study aggregation for dynamic treatment effects
- Multiplier bootstrap inference options
- Integration with existing visualization tools

Also add planned features note to triple_diff.py module docstring.

Author: claude

ROADMAP.md

@@ -44,12 +44,25 @@ Two-stage approach gaining traction in applied work. First residualizes outcomes
 
 ### Staggered Triple Difference (DDD)
 
-Extend the existing `TripleDifference` estimator to handle staggered adoption settings.
+Extend the existing `TripleDifference` estimator to handle staggered adoption settings. The current implementation handles 2-period DDD; this extends to multi-period designs.
 
+**Multi-period/Staggered Support:**
 - Group-time ATT(g,t) for DDD designs with variation in treatment timing
-- Event study aggregation for dynamic treatment effects
+- Handle settings where groups adopt at different times
 - Multiple comparison groups (never-treated, not-yet-treated in either dimension)
-- Bootstrap inference for staggered DDD
+- `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:**
+- 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`.
 

diff_diff/triple_diff.py

@@ -11,6 +11,18 @@
 and doubly robust estimators that correctly handle covariate adjustment,
 unlike naive implementations.
 
+Current Implementation (v1.3):
+    - 2-period DDD (pre/post binary time indicator)
+    - Regression adjustment, IPW, and doubly robust estimation
+    - Analytical standard errors with robust/cluster options
+    - Proper covariate handling
+
+Planned for v1.4 (see ROADMAP.md):
+    - Staggered adoption support (multiple treatment timing)
+    - Event study aggregation for dynamic treatment effects
+    - Multiplier bootstrap inference
+    - Integration with plot_event_study() visualization
+
 Reference:
     Ortiz-Villavicencio, M., & Sant'Anna, P. H. C. (2025).
     Better Understanding Triple Differences Estimators.