This question occurred to me while I was reading [Klartag's papers on central limit theorems for convex bodies](http://www.math.tau.ac.il/~klartagb/publications.html).


Given probability measures $\mu$, $\nu$ on (the Borel $\sigma$-field of) $R^d$ with finite first moments, their Wasserstein distance is given by:
$$W\_{R^d}(\mu,\nu) = \sup \mbox{ of }\int\_{R^d}f d\mu-\int\_{R^d}f d\nu\mbox{ over all 1-Lipschitz }f:R^d\to R.$$


(**NB**: there was an error in this formula -- an inf in the place of the sup.)


Given a vector $v\in R^d$, let $\mu\_v$ be the distribution of $X.v$, where $X$ has distribution $\mu$. Define $\nu\_v$ analogously. Note that $\mu\_v$ and $\nu\_v$ are distributions over $R$.


**Question:** is there a constant $C\_d>0$ depending on $d$ only such that: $$W\_{R^d}(\mu,\nu)\leq C\_d\sup\_{v\in R^d, |v|=1}W\_{R}(\mu\_v,\nu\_v)?$$
If so, how does $C\_d$ grow with $d$?


**An illustrative example:** Assume $Z$ is uniform over a $D-1$ dimensional sphere $S^{D-1}$ in $R^D$. Any one-dimensional marginal of $\sqrt{D-1}Z$ is approximatelly Gaussian. Now let $\mu$ be the law of the first $d$ coordinates of $\sqrt{D-1}Z$, and $\nu$ be the standard Gaussian distribution on $R^d$. Can one deduce from the previous statement alone that $\mu$ and $\nu$ are close?


**Another example:** Let $Z$ be a random vector in $R^D$ with mean $0$ and covariance matrix $I\_D$, $D\gg 1$. Old results of Sudakov (quoted [here](https://doi.org/10.1007/s004400050087)) show that "most" one-dimensional marginals of $Z$ are close to $|Z|N$ where $N$ is standard normal and independent from $Z$. A positive answer to the above question would lead to typical results for $d$-dimensional projections of $Z$.



Wasserstein distance in R^d from one dimensional marginals
There is a result which contains an answer to your question in a somewhat different form. Instead of the transportation metric it uses another metric which metrizes the weak topology in the space of measures on $\mathbb R^d$:
$$
\lambda(\mu,\nu) \le \delta \iff \exists\; T\ge 1/\delta : \langle \exp(i(t,\cdot)),\mu-\nu\rangle \le \delta \quad\forall\; |t|\le T \;,
$$
which might still be OK for your purposes. [This article](https://mathscinet.ams.org/mathscinet-getitem?mr=1485529 "Klebanov, L. B.; Rachev, S. T. Proximity of probability measures with common marginals in a finite number of directions.") by Klebanov and Rachev actually contains a stronger result (Theorem 4): it gives an explicit upper estimate for $\lambda(\mu,\nu)$ in terms of the maximal distance $\lambda(\mu\_v,\nu\_v)$ between the projections of $\mu$ and $\nu$ onto a finite (growing) number of directions $v$ in $\mathbb R^d$.


PS In spite of a sufficiently long tradition of misnaming the transportation metric (some people even go as far as calling it after Hutchinson), I would still insist on using the name of Kantorovich (or Monge-Kantorovich, Kantorovich-Rubinshtein), see, for instance, [this](https://encyclopediaofmath.org/index.php?title=Wasserstein_metric) historical article.


