I have random variables $X_1, X_2, \dots, X_n$ and $Y_1, Y_2, \dots, Y_n$, with $n$ a large integer. All pairs $(X_i, Y_i)$ are independent and identically distributed, but every $X_i$ and $Y_i$ within a pair are dependent. All $X_i$ and $Y_i$ yield positive real numbers.
I have a sample of each variable, I'll call the values $x_1, x_2, \dots, x_n$ and $y_1, y_2, \dots, y_n$. Then I can calculate $\mu_x = \frac{1}{n} \sum_{i=1}^n x_i$ and $\sigma_x = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_x)^2$, and similar formulas for $\mu_y$ and $\sigma_y$.
The goal is to estimate $\mu = E[X_1] / E[Y_1]$ and to get a confidence interval with a given confidence (for example 95%). I'm not sure if I may do some assumptions, since the random variables are the output of a process that I do not fully understand. Maybe I may assume that all variables are almost normally distributed, but it would be better if the question can be answered with no assumptions or weaker assumptions.
To estimate $\mu$ I can simply calculate $\mu \approx \mu_x / \mu_y$, but the confidence interval gives me headaches. How can I calculate the confidence interval?