Fix treatment_effects availability claim per PR review

Both TwoStageDiD and ImputationDiD provide treatment_effects
DataFrame — remove incorrect "unique to TwoStageDiD" language
and update summary table accordingly.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: igerber

docs/tutorials/12_two_stage_did.ipynb

@@ -96,7 +96,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Per-Observation Treatment Effects\n\nA feature unique to `TwoStageDiD` is the `treatment_effects` DataFrame, which contains one row per treated observation with:\n- `tau_hat`: the residualized outcome (actual outcome minus estimated counterfactual)\n- The unit and time columns (using the original column names from the input data, e.g., `unit` and `period`)\n- `rel_time`: relative time since treatment\n- `weight`: aggregation weight — `1/n_valid` for observations with finite `tau_hat`, `0` for NaN rows (e.g., rank-deficient cases)\n\nThis enables granular analysis: examining which units or periods drive the aggregate effect, detecting outliers, or constructing custom aggregation schemes."
+   "source": "## Per-Observation Treatment Effects\n\nBoth `TwoStageDiD` and `ImputationDiD` provide a `treatment_effects` DataFrame containing one row per treated observation with:\n- `tau_hat`: the residualized outcome (actual outcome minus estimated counterfactual)\n- The unit and time columns (using the original column names from the input data, e.g., `unit` and `period`)\n- `rel_time`: relative time since treatment\n- `weight`: aggregation weight — `1/n_valid` for observations with finite `tau_hat`, `0` for NaN rows (e.g., rank-deficient cases)\n\nThis enables granular analysis: examining which units or periods drive the aggregate effect, detecting outliers, or constructing custom aggregation schemes."
   },
   {
    "cell_type": "code",
@@ -237,7 +237,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Summary\n\n| Feature | TwoStageDiD | ImputationDiD | CallawaySantAnna |\n|---------|-------------|---------------|------------------|\n| **Approach** | Residualize via FE, regress on treatment | Impute Y(0) via FE model | Group-time ATT(g,t) |\n| **Point estimates** | Identical to ImputationDiD | Identical to TwoStageDiD | Different weighting |\n| **Variance** | GMM sandwich (influence function) | Conservative (Theorem 3) | Analytical influence function (optional bootstrap) |\n| **Per-obs effects** | Yes (`treatment_effects`) | No | No |\n| **Pre-trend test** | Via event study pre-periods | Yes (built-in F-test) | Via event study pre-periods |\n| **Best for** | Robustness check, granular effects | Maximum efficiency under homogeneity | Heterogeneous effects |\n\n**References:**\n- Gardner, J. (2022). Two-stage differences in differences. *arXiv:2207.05943*.\n- Butts, K. & Gardner, J. (2022). did2s: Two-Stage Difference-in-Differences. *R Journal*, 14(1), 162-173."
+   "source": "## Summary\n\n| Feature | TwoStageDiD | ImputationDiD | CallawaySantAnna |\n|---------|-------------|---------------|------------------|\n| **Approach** | Residualize via FE, regress on treatment | Impute Y(0) via FE model | Group-time ATT(g,t) |\n| **Point estimates** | Identical to ImputationDiD | Identical to TwoStageDiD | Different weighting |\n| **Variance** | GMM sandwich (influence function) | Conservative (Theorem 3) | Analytical influence function (optional bootstrap) |\n| **Per-obs effects** | Yes (`treatment_effects`) | Yes (`treatment_effects`) | No |\n| **Pre-trend test** | Via event study pre-periods | Yes (built-in F-test) | Via event study pre-periods |\n| **Best for** | Robustness check, granular effects | Maximum efficiency under homogeneity | Heterogeneous effects |\n\n**References:**\n- Gardner, J. (2022). Two-stage differences in differences. *arXiv:2207.05943*.\n- Butts, K. & Gardner, J. (2022). did2s: Two-Stage Difference-in-Differences. *R Journal*, 14(1), 162-173."
   }
  ],
  "metadata": {