fix cross reference

Author: drbenvincent

docs/source/notebooks/its_pymc.ipynb

@@ -10,7 +10,7 @@
     "Interrupted Time Series (ITS) analysis is a powerful approach for estimating the causal impact of an intervention or treatment when you have a single time series of observations. The key idea is to compare what actually happened after the intervention to what would have happened in the absence of the intervention (the \"counterfactual\"). To do this, we train a statistical model on the pre-intervention data (when no treatment has occurred) and then use this model to forecast the expected outcomes into the post-intervention period as-if treatment had not occurred. The difference between the observed outcomes and these model-based counterfactual predictions provides an estimate of the causal effect of the intervention, along with a measure of uncertainty if using a Bayesian approach.\n",
     "\n",
     ":::{note}\n",
-    "**Point intervention vs fixed-period intervention**: This notebook focuses on a **point intervention**, an intervention that occurs at a specific time and continues indefinitely (or at least through the end of the observation period). For temporary interventions with defined start and end times (a **fixed-period intervention**), you can specify `treatment_end_time` to enable a three-period analysis. This splits the post-intervention period into an intervention period (when treatment is active) and a post-intervention period (after treatment ends), enabling analysis of effect persistence and decay. See {doc}`its_three_period_pymc` for a complete example of fixed-period interventions.\n",
+    "**Point intervention vs fixed-period intervention**: This notebook focuses on a **point intervention**, an intervention that occurs at a specific time and continues indefinitely (or at least through the end of the observation period). For temporary interventions with defined start and end times (a **fixed-period intervention**), you can specify `treatment_end_time` to enable a three-period analysis. This splits the post-intervention period into an intervention period (when treatment is active) and a post-intervention period (after treatment ends), enabling analysis of effect persistence and decay. See {doc}`its_post_intervention_analysis` for a complete example of fixed-period interventions.\n",
     ":::"
    ]
   },