Why do we need Jensen inequality for variational autoencoders? Just to clarify, I think I understand all the derivations in context of VAEs pretty well; however, there is one last thing that I need explained. There are multiple related derivations of the evidence lower bound (ELBO).
The following uses Jensen's Inequality to form the bound.
$$
\begin{align}
    & \operatorname*{argmax}_\Theta \log \mathbb{E}_{z \sim q(z|x)}[p(x|z) * \frac{p(z)}{q(z|x)}] \\
    &\geq \operatorname*{argmax}_\Theta \mathbb{E}_{z \sim q(z|x)}[\log(p(x|z) * \frac{p(z)}{q(z|x)})]  && \text{Jensen's inequality} \\
    &= \operatorname*{argmax}_\Theta \mathbb{E}_{z \sim q(z|x)}[\log p(x|z) + \log p(z) - \log q(z|x)] \\
    &= \operatorname*{argmax}_\Theta \mathbb{E}_{z \sim q(z|x)}[\log p(x|z)] + \mathbb{E}_{z \sim q(z|x)}[\log p(z) - \log q(z|x)] \\
    &= \operatorname*{argmax}_\Theta \mathbb{E}_{z \sim q(z|x)}\left[\log p(x|z)\right] - D_{KL}[q(z|x)\parallel p(z)]
\end{align}
$$
However, is there a reason why we need to apply Jensen's Inequality here? Can't we just sample approximate the term like it is? Is it purely because of numerical issues, or is there some math rule I overlooked that prohibits directly sampling from the inner expectation to approximate?
I have found a related answer that implies that we actually do not need this lower bound in general Why is computing $\log p(x)$ difficult, but not the ELBO?
Here is first idea why Jensen's is necessary: Let's assume we want to optimize for example with batched stochastic gradient descent.
$$
\begin{align}
    & \mathbb{E}_{x \sim D} \log \mathbb{E}_{z \sim q(z|x)}[p(x|z) * \frac{p(z)}{q(z|x)}] \\
    & \approx  \frac{1}{N} \sum_{i = 0}^N \log \mathbb{E}_{z \sim q(z|x_i)}[p(x_i|z) * \frac{p(z)}{q(z|x_i)}] \\
    & \approx  \frac{1}{N} \sum_{i = 0}^N \log  \frac{1}{M}\sum_{j=0}^{M} [p(x_i|z_j) * \frac{p(z_j)}{q(z_j|x_i)}]
\end{align} 
$$
We can approximate the outer expectation over the data by sampling N times from the datasets and meaning the results. We do the same for the inner expectation and just assume that this is a correct approximation. However, if we for example sample the same sample multiple times from the outer expectation and only a single from the inner, we actually approximated the sum of log probabilities (so the result of applying Jensen's) instead of the log of a sum of probabilities.
EDIT1:
If we increase M to infinity, we recover the original formulation. This should be what also is stated in Burda et al., 2015 in equation 10 (https://arxiv.org/pdf/1509.00519.pdf)
At least these are my thoughts and maybe someone can show this formally instead of intuitively like I did.
EDIT2: I think the inverse of proof 1.3 in Burda et al. 2015 in Appendix A is what my original question aimed for. So if we start from the expectations and approximate expectation inside the log by the mean it must be a lower-bound on the original log p(x) since Jensen's applies. I will add this as an answer to this thread soon.
 A: 
Is there a reason why we need to apply Jensen's Inequality here? Can't we just sample approximate the term like it is?

There are two possibilities for what you’re asking here. I’ll address both.

*

*Why do we need Jensen’s inequality?
To ensure that this is in fact a bound. If the optimization
objective weren’t a bound, then there wouldn’t be much point in
optimizing it. Speaking loosely, think of lifting a handful of sand.
If it’s not a lower bound, sand slips through the gaps between your
fingers.

*Why do we need to bound the likelihood (using Jensen’s inequality) instead of optimizing it directly by a Monte Carlo method?
Well, we assume that the joint probability factors as
$p(x,z)=p(z)p(x\mid z)$. That posterior is the (computationally)
hard part. In general, we can’t assume that we can normalize it. But
maybe we can sample from it? In fact, what you’re proposing is
common! MCMC is often used to approximate the posterior. But if
there’s a large amount of data or a complex model, that becomes very
slow. That’s when people switch to VI.

