docs(paper): clarify local estimator API in Software Design

paper/paper.md

@@ -62,7 +62,7 @@ A standalone package is warranted because existing libraries are organized aroun
 - **`SimpleStratifiedDistributionEstimator`** and **`AdjustedStratifiedDistributionEstimator`**: For covariate-adaptive randomization designs, implementing methods from @byambadalai2025efficientestimationdistributionaltreatment.
 - **`SimpleLocalDistributionEstimator`** and **`AdjustedLocalDistributionEstimator`**: For settings with imperfect compliance, implementing methods from @byambadalai2025imperfectcompliance.
 
-All estimators implement a consistent API with three primary methods: `predict_dte()` for distributional treatment effects, `predict_pte()` for probability treatment effects over intervals, and `predict_qte()` for quantile treatment effects. The adjusted estimators use K-fold cross-fitting to prevent overfitting and support both single-task and multi-task learning modes [@hirata2025efficientscalableestimationdistributional] for computational efficiency. Bootstrap methods provide confidence intervals with multiple variance estimation approaches.
+The stratified and non-stratified estimators share a common API with three methods: `predict_dte()` for distributional treatment effects, `predict_pte()` for probability treatment effects over intervals, and `predict_qte()` for quantile treatment effects. The local estimators expose `predict_ldte()` and `predict_lpte()` instead, for the imperfect-compliance setting. The adjusted estimators use K-fold cross-fitting to prevent overfitting and support both single-task and multi-task learning modes [@hirata2025efficientscalableestimationdistributional] for computational efficiency. Bootstrap methods provide confidence intervals with multiple variance estimation approaches.
 
 The template method pattern is a natural fit here because every estimator shares the same outer algorithm (evaluate a distribution function on a grid, difference across treatment arms, and construct confidence bands) and differs only in how the conditional distribution is estimated, which depends on the experimental design and on whether a plug-in or a cross-fitted machine learning estimator is used. Defining the outer algorithm once in the base class keeps its statistical invariants in one place, whereas a strategy-based configuration would push that structure into runtime flags and obscure them. For the same reason the estimators are exposed as distinct classes rather than a single configurable one: many flag combinations would be confusing (e.g., stratification without strata) or correspond to different estimands with different identifying assumptions, and separate classes make the required inputs explicit at construction time and let each estimator evolve independently.