Note that index $j$ refers to decision maker $\human$. Parameter$\alpha_\human$ provides for the leniency of a decision maker by $\logit(\leniencyValue_\human)$.
Note that we are making the simplifying assumption that coefficients $\gamma$ are the same for all defendants, but decision makers are allowed to differ in intercept $\alpha_j\approx\logit(\leniencyValue_j)$ so as to model varying leniency levels among them. % (Eq. \ref{eq:leniencymodel}).
Intercept$\alpha_\human$ provides for the leniency of a decision maker $\human$by $\logit(\leniencyValue_\human)$.
Note that we are making the simplifying assumption that coefficients $\gamma$ are the same for all defendants, but decision makers are allowed to differ in intercept $\alpha_\human\approx\logit(\leniencyValue_\human)$ so as to model varying leniency levels among them. % (Eq. \ref{eq:leniencymodel}).
%The decision makers in the data differ from each other only by leniency.
%\noindent
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@@ -219,7 +219,7 @@ We use prior distributions given in Appendix~X to ensure the identifiability of
%\spara{Computing counterfactuals}
For the model defined above, the counterfactual $\hat{Y}$ can be computed by the approach of Pearl.
For fully defined model (fixed parameters) $\hat{Y}$ can be determined by the following expression:
For fully defined model (fixed parameters) the counterfactual expectation can be determined by the following expression:
