add admonition box about the parallel trends assumption + add glossary term

Author: drbenvincent

docs/source/glossary.rst

@@ -53,6 +53,9 @@ Glossary
    One-group posttest-only design
       A design where a single group is exposed to a treatment and assessed on an outcome measure. There is no pretest measure or comparison group.
 
+   Parallel trends assumption
+      An assumption made in difference in differences designs that the trends (over time) in the outcome variable would have been the same between the treatment and control groups in the absence of the treatment.
+
    Panel data
       Time series data collected on multiple units where the same units are observed at each time point.
 

docs/source/quasi_dags.ipynb

@@ -331,7 +331,11 @@
                 "\n",
                 "The causal effect of the treatment upon the outcome is typically estimated by fitting a regression model of the form `y ~ time + group + time:group`. The interaction term `time:group` captures the causal effect of $Z \\rightarrow Y$. \n",
                 "\n",
-                "We can note that this interaction term $\\text{time} \\times \\text{group}$ encodes the values of $Z$, which as we said above, is equal to 1 for only the treatment group at time 1. So another way to think about the inclusion of an interaction effect is that we are simply conditioning on all the observed data ($Z$, $\\text{time}$, $\\text{group}$, $Y$) to estimate the causal effect of $Z \\rightarrow Y$."
+                "We can note that this interaction term $\\text{time} \\times \\text{group}$ encodes the values of $Z$, which as we said above, is equal to 1 for only the treatment group at time 1. So another way to think about the inclusion of an interaction effect is that we are simply conditioning on all the observed data ($Z$, $\\text{time}$, $\\text{group}$, $Y$) to estimate the causal effect of $Z \\rightarrow Y$.\n",
+                "\n",
+                ":::{warning}\n",
+                "Achieving an unbiased estimate is strongly dependent upon the {term}`parallel trends assumption`. That is, we assume that the treatment and control groups would have followed the same trajectory (over time) in the absence of treatment. This is a strong assumption and should be carefully considered when interpreting the results of a difference-in-differences study. In the case of the classic 2$\\times$2 design we cannot assess the validity of this assumption empirically, so it is important to consider the plausibility of this assumption in the context of the particular example. \n",
+                ":::"
             ]
         },
         {