Why are superlearners used in TMLE? When estimating causal properties using TMLE, what is the benefit of using superlearners? It seems like there's an assumption that the model that minimizes prediction error is also the best one for causal inference and I don't understand why that is.
For example in this paper, the authors show that the lasso model which is optimal for prediction will contain a lot of noise variables. Therefore minimizing prediction error will increase model misspecification. Isn't it possible that the same thing happens with superlearners, where the model is incorrect but makes good predictions?

Minimizing the prediction error of nuisance parameters is (generally) the correct goal when developing efficient estimators using e.g. estimating equations or one-step estimators. To suggest this, let's rigorously focus on a case study: estimating $\mu:=E\{AY/\pi(W)\}$ in a nonparametric model, for $A$ binary and $\pi(w) = P(A=1\mid W=w)$ the propensity score.
Define $Q(a,w) = E(Y \mid A=a, W=w)$ as the outcome regression. The nonparametric efficient influence curve is $$D^*(W,A,Y; P) = \frac{A}{\pi(W)}Y - \frac{A-\pi(W)}{\pi(W)} Q(1,W) - \mu.$$ With i.i.d. sampling, an efficient estimator is $$\hat\mu_n = \frac{1}{n}\sum_{i=1}^n \left\{ \frac{A}{\hat\pi(W)}Y - \frac{A-\hat\pi(W)}{\hat\pi(W)} \hat{Q}(1,W) \right\},$$ whenever cross-fitting is used and the remainder is negligible, i.e. $$E\left[ \frac{\{\hat\pi_n(W) - \pi_0(W)\} \{\hat{Q}_n(1,W) - Q_0(1,W)\}}{\hat\pi_n(W)} \right] = o_p(n^{-1/2}).$$ Since the left hand side is upper bounded by $$\|\hat\pi_n(W) - \pi_0(W)\| \|\hat{Q}_n(1,W) - Q_0(1,W)\|,$$ whenever sample positivity holds, we can see that we should fit the nuisance parameters by ensuring the estimates are close to the truth. Further, recall that minimizing closeness to a true expectation is equivalent to minimizing a prediction error. We can for example accomplish this by minimizing the cross validated error.
Although the TMLE is a different estimator, its influence function is the same and the same considerations apply. We can see this through the following motivation.
The superlearner is nicely described by Noah. It's purpose is to find nuisance parameters which ensure the above norms are as small as possible. As Noah explains, it does this by looking through several estimators and finding which to prefer. From above, we can see that the product of the rate of convergence of both estimators must be $o_p(n^{-1/2})$; since the superlearner inherits the best rate of convergence of its constituent estimators, it can be helpful here.
