remove lame reference section

Author: drbenvincent

docs/source/notebooks/its_lift_test.ipynb

@@ -1552,15 +1552,7 @@
     "\n",
     "The method shown here is particularly valuable for national-level campaigns where geographic controls don't exist, but it can be extended in several ways. Multiple lift tests can be accumulated over time—testing different channels, different spend levels, and different time periods—to build a robust calibration dataset. The ITS model itself can be enhanced by incorporating additional predictors like weather, competitor activity, or special events to improve the counterfactual's accuracy. For organizations with multiple products or sub-markets, hierarchical Bayesian models can pool information across units while still estimating unit-specific lift.\n",
     "\n",
-    "Ultimately, this approach represents a practical solution to one of marketing's most challenging problems: measuring incrementality when traditional experimental designs aren't feasible. By combining the strengths of time series analysis with Bayesian uncertainty quantification, we can generate the experimental evidence needed to build better attribution models and make more confident investment decisions.\n",
-    ":::\n",
-    "\n",
-    "### References\n",
-    "\n",
-    "- [PyMC-Marketing Lift Test Calibration](https://www.pymc-marketing.io/en/latest/notebooks/mmm/mmm_lift_test.html) - How to use these results in MMM\n",
-    "- [PyMC-Marketing MMM Example](https://www.pymc-marketing.io/en/latest/notebooks/mmm/mmm_example.html) - Full MMM workflow\n",
-    "- Jin, Yuxue, et al. \"Bayesian methods for media mix modeling with carryover and shape effects.\" (2017)\n",
-    "- [CausalPy Documentation](https://causalpy.readthedocs.io/) - More causal inference methods\n"
+    "Ultimately, this approach represents a practical solution to one of marketing's most challenging problems: measuring incrementality when traditional experimental designs aren't feasible. By combining the strengths of time series analysis with Bayesian uncertainty quantification, we can generate the experimental evidence needed to build better attribution models and make more confident investment decisions."
    ]
   },
   {