Plot a histogram of pre-treatment pollution for treated and control households. What do you see? Re-do your balance table to reflect any necessary adjustments. What does this table tell you about whether or not CHICAGO's randomization worked? What assumption do we need to make on unobserved characteristics in order to be able to estimate the causal effect of chicago_trt? count 900- 600- 300- 0- 0 500 baseline_pollution 1000 count 5000- 4000- 3000- 2000- 1000 - 0- 0.0e+00 5.0e+06 1.0e+07 1.5e+07 2.0e+07 baseline_pollution regressions % select("baseline_hhsize", "baseline_charcoal_use", "baseline_pollution", "baseline_hh_head_age") %>% lapply(function(x) lm(x ~ chicago_trt, data = data)) balance_table % sapply(function(x) coef(summary(x))[c(2,8)]) %>% t() balance_table %>% kable(col.names = c("Differences in means", "p-value")) %>% "center", font_size = 20, latex_options = "hold_position")| kable_styling(position Differences in means p-value baseline_hhsize baseline_charcoal_use 0.0155866 0.7021534 0.0772821 0.4430097 baseline_pollution -3998.6675218 0.3174927 baseline_hh_head_age -0.0719880 0.4720638 Questions: You have been asked by a well-meaning NGO, the Cleaner Household Indoor Cooking to Aid Growth Org (CHICAGO) to help them learn about the impacts of their flagship program. CHICAGO provides households with low-cost improved cookstoves, and hypothesizes that these energy efficient stoves reduce indoor air pollution.