  We observe $n$ pairs of independent (but not necessarily i.i.d.) random
variables $X_{1}=(W_{1},Y_{1}),\ldots,X_{n}=(W_{n},Y_{n})$ and tackle the
problem of estimating the conditional distributions $Q_{i}^{\star}(w_{i})$ of
$Y_{i}$ given $W_{i}=w_{i}$ for all $i\in\{1,\ldots,n\}$. Even though these
might not be true, we base our estimator on the assumptions that the data are
i.i.d.\ and the conditional distributions of $Y_{i}$ given $W_{i}=w_{i}$ belong
to a one parameter exponential family $\bar{\mathscr{Q}}$ with parameter space
given by an interval $I$. More precisely, we pretend that these conditional
distributions take the form $Q_{{\boldsymbol{\theta}}(w_{i})}\in
\bar{\mathscr{Q}}$ for some ${\boldsymbol{\theta}}$ that belongs to a VC-class
$\bar{\boldsymbol{\Theta}}$ of functions with values in $I$. For each
$i\in\{1,\ldots,n\}$, we estimate $Q_{i}^{\star}(w_{i})$ by a distribution of
the same form, i.e.\ $Q_{\hat{\boldsymbol{\theta}}(w_{i})}\in
\bar{\mathscr{Q}}$, where $\hat {\boldsymbol{\theta}}=\hat
{\boldsymbol{\theta}}(X_{1},\ldots,X_{n})$ is a well-chosen estimator with
values in $\bar{\boldsymbol{\Theta}}$. We show that our estimation strategy is
robust to model misspecification, contamination and the presence of outliers.
Besides, we provide an algorithm for calculating $\hat{\boldsymbol{\theta}}$
when $\bar{\boldsymbol{\Theta}}$ is a VC-class of functions of low or moderate
dimension and we carry out a simulation study to compare the performance of
$\hat{\boldsymbol{\theta}}$ to that of the MLE and median-based estimators.
