Lasso (or Ridge) vs Bayesian MAP This is the first time I have posted here. I am looking for some feedback or perspective on this question.
To make it simple, let's just talk about linear models. We know the MLE solution for the $l_1$ loss objective is the same as the Bayesian MAP estimate with a Laplace prior for each parameter. I'll show it here for convenience.
For vector $Y$ with $n$ observations, matrix $X$, parameters $\beta$, and noise $\epsilon$
$$Y = X\beta + \epsilon,$$
the standard lasso parameter estimate is
$$\hat{\beta}_{l_1} = \arg \min_\beta \sum_{i=1}^n (y_i - \beta^Tx_i)^2 + \lambda\sum_{j=1}^p|\beta_j|.$$
We can instead consider the MAP estimate solution
$$\hat{\beta}_{MAP} = \arg \max_\beta \prod_{i=1}^n P(\epsilon_i|\beta)P(\beta),$$
where under mean 0 Normal noise $\epsilon$ and mean 0 Laplace priors for $\beta$ we have
$$\hat{\beta}_{MAP} = \arg \max_\beta \log \left(\prod_{i=1}^n \dfrac{1}{\sigma\sqrt{2\pi}}e^{-\dfrac{(y_i-\beta^Tx_i)^2}{2\sigma^2}}\right) + \log\left(\prod_{j=1}^p\dfrac{1}{2b}e^{-\dfrac{|\beta_j|}{b}}\right)$$
and after simplifying and substituting $\lambda = \dfrac{1}{b}$ we obtain the MAP estimate
$$\hat{\beta}_{MAP} = \arg \min_\beta \sum_{i=1}^n \left(y_i - \beta^Tx_i\right)^2 + \lambda \sum_{j=1}^p |\beta_j| = \hat{\beta}_{l_1}.$$
However, I think there is a subtle nuance that is not captured and was curious if anyone else has insight on this. What I find in practice is that I can typically outperform a Lasso by making insightful variable selections. Essentially, I remove the variables for which I believe a regression will most likely only find high variance parameter estimates. By doing so, I am placing a very informative prior on variables that I eliminate and a very uninformative prior on variables I decide to keep. And this was peculiar to me because I was casually aware of this proof.
I think the main nuance is that $b$, the Laplace variance, is shared by all parameters in this frame work, but when I am selecting things, it is not. I briefly also considered the implications of unquestionably rescaling all $X$ variables to 0 mean and unit variance, but I don't think that impacts the priors on $\beta$ so that they all come from a Laplace with 0 mean and $b$ scale.
However, I did not show it was the case. I am not sure what happens to $\beta_j = \dfrac{\text{Cov}(X_j,Y)}{\text{Cov}(X_j,X_j)}$ when you transform $\tilde{X_j} = \dfrac{X_j - \bar{X_j}}{\sigma_{X_j}}$. Essentially, $\tilde{\beta}_j = \dfrac{\text{Cov}(\tilde{X_j},Y)}{\text{Cov}(\tilde{X_j},\tilde{X_j})} = \dfrac{\sigma_{X_j}\text{Cov}(X_j,Y)}{\text{Cov}(X_j,X_j)} $. Does this rescaling give reasonable grounds to say all $\beta$ come from the same Laplace distribution? If so, why do I still beat out the Lasso? Did I implement the Lasso wrong or cheat?
This is the best explanation I can come up with for why my informative but crude variable selection/Bayesian method outperforms the Lasso even though the Lasso is Bayes-esque. Very curious if anyone else has insights on other things to consider or can show the effects of rescaling $X$ variables. Thanks.

When you select variables using your "crude" method, this amounts to placing a prior on your coefficients that says that one specific subset of coefficients is (a priori) certainly equal to 0, and the others are (a priori) entirely unknown (i.e. a uniform prior).
So the question is: is this a better prior than the Laplace prior that is implied by a lasso regression? If you have good reason to believe that these coefficients are 0, then the answer is probably yes. The Laplace prior is less informative than the prior you impose in the selection method. It only says "generally, most coefficients are small". It doesn't say which coefficients in particular are small, and it doesn't require them to be exactly 0 either (even if it will push them in that direction).
Imposing a more informative prior will always improve performance (in expectation), if that informative prior is accurate. Basically, any prior knowledge you have, you should use. Using the less specific Laplace prior discards information. Of course, this only works if your prior is accurate. It is not the case that any manual variable selection will beat Laplace. If you discard the wrong variables, Laplace will beat your selection.
As for scaling, this can definitely make a big difference. If one of your independent variables happens to be in very large units, while the others are not, then you can generally expect the coefficient on that variable to be much smaller than the others. Not because it explains less variance, but because of the difference in scale. What you want your Laplace prior to do is shrink to zero those coefficients of variables that explain little variance in the data. To do this, it helps to ensure that all explanatory variables are scaled to have similar variances, so that their coefficients are proportional to how much variance they explain in the data. This allows the Laplace prior to capture the inductive bias that only some variables are necessary to explain the data. Without proper scaling, the Laplace prior becomes basically meaningless, as the relative magnitudes of the coefficients are then essentially arbitrary, and so their distribution has nothing to do with how much variance they explain.
