Suppose Di is an observed binary variable equal to one if an individual is assigned to treat- ment and equal to zero otherwise. Further, suppose Y; is an observed variable measuring some outcome of interest, that is hypothesized to be affected by Di. Recall that we express Y; in terms of the potential outcomes Yli and You as Yi Yli if Di = 1 = You if Di = 0 This means that if Di = 0, we observe You, while Yl; is the unobserved counterfactual outcome. Similarly, if Di = 1, we observe Y1i, while You is the unobserved counterfactual outcome.(6.2) In this setup, Yh- Yh- is the causal eect of treatment for individual 2'. However, for any given individual we observe either Y1.- or Ym, not both. Of course, we would still like to use linear regression to estimate the causal effect of treatment. Assume the following: 5 = Yn' Yo; 05 = Eli/6.] \"Us = Yo; Ell/01:] Note that since there is no 2' subscript on 5, we are assuming that the causal effect of treatment is the same for every individual. Show that we can write a simple regression model for Y. as Yi=a+D+ui