Consider the treatment-outcome model \(y=\) \(\mathbf{x}^{\prime} \boldsymbol{\beta}+\alpha d+\varepsilon\), where \(d\) is a binary indicator variable taking the value 1 if treatment is assigned randomly and 0 if treatment is not assigned (also randomly).

(a) Is randomized treatment a sufficient condition for identification of \(\alpha\) ?

(b) Is randomized treatment a sufficient condition for identification of \(\alpha\) and \(\beta\) ?