You can get 95% of the way to the path integral just using a single fact about the green's function [itex]g(x,t,x_0,t_0)[/itex]:
If [itex]t_1[/itex] is any time between [itex]t_0[/itex] and [itex]t[/itex], then
[itex]g(x,t,x_0, t_0) = \int dx_1 g(x_1, t_1, x_0, t_0) g(x,t,x_1, t_1)[/itex]
Iterating this [itex]N[/itex] times gives:
[itex]g(x,t,x_0, t_0) = \int dx_1 dx_2 dx_3 ... dx_N g(x_1, t_1, x_0, t_0) g(x_2, t_2, x_1, t_1) ... g(x, t, x_N, t_N)[/itex]
This is exactly true, no approximations or questionable limits. Then we can take the next step of saying, for [itex]\delta t[/itex] small, we can approximate
[itex]g(x+\delta x, t+\delta t, x, t) \approx A(\delta t) e^{\frac{i}{\hbar} L(x, \frac{\delta x}{\delta t}, t) \delta t}[/itex], where [itex]L(x,\dot{x},t)[/itex] is the classical Lagrangian and where [itex]A(\delta t)[/itex] is chosen to normalize [itex]g[/itex] correctly. With this approximation, we can rewrite the above:
[itex]g(x,t,x_0, t_0) \approx \int dx_1 A(\delta t_1) dx_2 A(\delta t_2) dx_3 ... dx_N A(\delta t_N) e^{\frac{i}{\hbar} \sum_j L(x_j, v_j, t_j) \delta t_j}[/itex] where [itex]\delta t_j = t_{j} - t_{j-1}[/itex], and with [itex]v_j = \frac{x_j - x_{j-1}}{\delta t_j}[/itex]
The next step is to appoximate the discrete sum by an integral:
[itex]\sum_j L(x_j, v_j, t_j) \delta t_j \approx \int dt L(x,\frac{dx}{dt}, t)[/itex]
where [itex]x(t)[/itex] is chosen to be a function that smoothly extrapolates [itex]x(t_j) = x_j[/itex].
So we have a finite approximation:[itex]g(x,t,x_0, t_0) \approx \int dx_1 A(\delta t_1) dx_2 A(\delta t_2) dx_3 ... dx_N A(\delta t_N) e^{\frac{i}{\hbar} \int dt L(x,\frac{dx}{dt}, t)}[/itex]
This is pretty much noncontroversial. The only issue is whether there is some sense in which the right-hand side can be said to "coverge" to something in the limit as [itex]N \rightarrow \infty[/itex]