6. [ 3 points] The CIA assumption is an assumption... Obvious, but the main issue is that it can never be tested. Thus, without a randomized controlled trail we, should be skeptical that the assumption holds. One way to to strengthen the argument is to provide additional robustness checks. The author reflects on Table 3 : These findings are compelling evidence that environmental factors are responsible for the correlation between water quality and digestive cancer rates. In particular, if water quality did not directly affect digestive cancer rates but was instead reflecting an unobserved correlation between water quality and omitted factors, such as occupational exposure to carcinogens, one would expect to find larger elasticities for men, who are more likely to work in mines and other hazardous occupations. To investigate this, the author runs regressions where the dependent variable is calculated separately for men and women. In particular, run the long regression above, but replace the dependent variable In_digestive with In_digestive_f (women) and ln_digestive_m (men). What do you find? 6. [ 3 points] The CIA assumption is an assumption... Obvious, but the main issue is that it can never be tested. Thus, without a randomized controlled trail we, should be skeptical that the assumption holds. One way to to strengthen the argument is to provide additional robustness checks. The author reflects on Table 3 : These findings are compelling evidence that environmental factors are responsible for the correlation between water quality and digestive cancer rates. In particular, if water quality did not directly affect digestive cancer rates but was instead reflecting an unobserved correlation between water quality and omitted factors, such as occupational exposure to carcinogens, one would expect to find larger elasticities for men, who are more likely to work in mines and other hazardous occupations. To investigate this, the author runs regressions where the dependent variable is calculated separately for men and women. In particular, run the long regression above, but replace the dependent variable In_digestive with In_digestive_f (women) and ln_digestive_m (men). What do you find