Fix figure 1 filename: hillstorm_dte.png (not hillstrom)

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

Author: TomeHirata

paper/paper.md

@@ -60,7 +60,7 @@ In the R ecosystem, packages like `qte` provide quantile treatment effect estima
 
 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.
 
-![Distributional treatment effects for the Hillstrom email marketing dataset [@hillstrom2008], comparing Women's vs Men's email campaigns. The simple estimator (left, purple) and ML-adjusted estimator (right, green) show that adjustment substantially tightens confidence bands, demonstrating the variance reduction benefit of regression adjustment.](hillstrom_dte.png)
+![Distributional treatment effects for the Hillstrom email marketing dataset [@hillstrom2008], comparing Women's vs Men's email campaigns. The simple estimator (left, purple) and ML-adjusted estimator (right, green) show that adjustment substantially tightens confidence bands, demonstrating the variance reduction benefit of regression adjustment.](hillstorm_dte.png)
 
 ![Local distributional treatment effects for emergency department costs in the Oregon Health Insurance Experiment [@finkelstein2012], estimated using `SimpleLocalDistributionEstimator` (left) and `AdjustedLocalDistributionEstimator` (right). Health insurance coverage shifts the distribution of ED costs, with ML adjustment again yielding narrower confidence intervals.](oregon_ldte_costs_comparison.png)