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Question 3 (33 pts.) ichael is a health economics student interested in estimating the effect of health insurance coverage D, on the number of doctor visits n by estimating the population regression function (PRF) Y- + B2Di+ u by using the sample reported in Table 2. Enrollment in a health insurance plan is voluntary. nok Table 2 7 72 Michael has heard that observational studies produce blased results, while randomized experiments deliver the causal effect if an exhaustive set of samples are produced. He conducts a set of experiments, which are accurately reported in Table 3. However, Michaels grasp of the analysis of randomized experiments is somewhat shaky and he needs assistance in answering a set of questions. Table 3 t iirdinaqu Treatment group(TLI)一1controlgroup (TL.O) Sample 1 0 Sample 2 Sample 4 Sample 5 Sample 6 a) (2 pts) Michael would like to use his experimental results in Table 3 to decompose Table 2 into the causal effest and the selection bias, List the individuals and the samples in Table 3 that must be used for the computation of the causal effect in Table 2. with ind pdus with ut sindivi ouals u.los Indinsuok 2 in nie ent in hcgsh insusance with indirial 3 withur Som
558100,50228/A (2 pts.) Compute the selection bias. In your answer, first expand each expected value by using the appropriate letters and subscripts for each observation and then substitute the corresponding numerical values. iv. (2 pts.) Briefly explain why the causal effect and the observed effect are of the same (opposite) sign. e) 1 (2 pts.) Demonstrate that the indicator variable Ti could be re-written as the population regression function (PRF).Y.. α +pT + u. 8) (2 pts.) Demonstrate that the observed effect equals the causal effect in the PRF in f) if h) (2 pts.) Briefly explain why randomizing enrollment into a health insurance plan ensures that
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