If we have two joint distributions on $(X,Y)$, $P_{X,Y}$ and $Q_{X,Y}$ that are close in $L^1$ or "total variation" with $\|P_{X,Y}-Q_{X,Y}\|_1<\varepsilon$ then:

  • Are the distributions on the conditional expectations $P_{E[X|Y]}, Q_{E[X|Y]}$ also close in $L^1$, e.g. $\|P_{E[X|Y]}-Q_{E[X|Y]}\|_1<N\varepsilon$?

  • In particular, what about the case where $Y=X+Z$ with $X\perp Z$?

  • If $X,Y$ are finite mean and variance, then are the means and variances of $E[X|Y]$ under the two distributions close?

Assuming they are all continuous RVs, the difference is:

\begin{align} \left|P_{E[X|Y]}(w)-Q_{E[X|Y]}(w)\right| &= \left|\int_{y\in A_P} P_Y(y) dy -\int_{y\in A_Q } Q_Y(y) dy\right| \end{align}

with $A_P=\left\{y:\int x P(X=x|Y=y) dx = w\right\},\ A_Q=\left\{y:\int x Q(X=x|Y=y) dx = w\right\}.$

I can't figure out how to make the difference between the two sets small, which makes me suspicious that conditional expectation is unfortunately not continuous in this sense.

  • $\begingroup$ Are you still interested in the question? If so, I can try and help by placing a bounty on it (I am curious myself). $\endgroup$ – Clement C. May 12 '17 at 13:40
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    $\begingroup$ Clement, yes I am definitely still interested in proofs or counterexamples. With weak conditions $E[X|Y]$ turns out to be the MMSE estimator, and it would be nice to know that these estimators are stable up to small permutations of the underlying distributions. $\endgroup$ – Christian Chapman May 12 '17 at 23:04
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    $\begingroup$ Here we go, then. $\endgroup$ – Clement C. May 12 '17 at 23:05

For a counterexample for your first two cases, let $X,Z$ be independent Rademacher random variables and $Y = X+Z$. Under $P$, suppose $X$ is Rademacher($p$) for some $p$ (i.e. $P(X=1) = p$, $P(X=-1)=1-p$), and $Z$ is Rademacher(1/2). Under $Q$, suppose $X,Z$ are both Rademacher(1/2). In each case, take $Y=X+Z$ as you suggest.

Now you can check that $$P_{X,Y} = \frac{p}{2} (\delta_{(1,2)} + \delta_{(1,0)}) + \frac{1-p}{2} (\delta_{(-1,0)} +\delta_{(-1,-2)})$$ and $Q_{X,Y}$ is the same with $p=1/2$. You may compute directly that the total variation distance between $P_{X,Y}$ and $Q_{X,Y}$ is $|1-2p|$ and in particular it goes to zero as $p \to 1/2$.

Next, under $P$, note that $$E[X \mid Y] = \begin{cases} 1, & Y=2 \\ -1, & Y = -2 \\ p - \frac{1}{2}, & Y=0 \end{cases}$$
where these events have probabilities $p/2$, $(1-p)/2$, $1/2$ respectively. Hence $$P_{E[X \mid Y]} = \frac{p}{2} \delta_1 + \frac{1-p}{2} \delta_{-1} + \frac{1}{2} \delta_{p - \frac{1}{2}}$$ and $Q_{E[X \mid Y]}$ is the same with $p=1/2$. In particular, for $p \ne 1/2$ the total variation distance between $P_{E[X \mid Y]}$ and $Q_{E[X \mid Y]}$ is always at least 1.

For the third question, as far as means, the "conditional" part is really irrelevant, so we can take $Y=0$ in all cases. Fix $n$ and suppose that $P(X=n) = 1/n$, $P(X=0) = 1-1/n$, and $Q(X=0)=1$. Then the total variation distance between $P_X$ and $Q_X$ (and likewise $P_{X,Y}, Q_{X,Y}$) is $2/n$, which goes to 0 as $n \to \infty$. But under $P$, we have $E[X \mid Y] = E[X] = 1$ for any $n$, while under $Q$ it is 0.

The basic problem is that for any state space $E$, the total variation topology on the space $\mathcal{P}(E)$ of probability measures on $E$ is not able to detect the topology of $E$, but only its measurable structure. So it cannot tell whether two points of $E$ are close together or far away, but only whether or not they are the same point. This is why the weak topology is more useful for most purposes.

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  • $\begingroup$ This is a second-order preoccupation, but for the sake of intuition and understanding, is there any chance you could elaborate on your last paragraph? (Not necessarily a lot) $\endgroup$ – Clement C. May 16 '17 at 12:02
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    $\begingroup$ @ClementC. For instance, with $E = \mathbb{R}$, if you let $\mu_n$ be a point mass at $1/n$, the sequence $\mu_n$ fails to converge in total variation (since $\|\mu_n - \mu_m\|=2$ for all $n \ne m$, but in the weak topology it converges to a point mass at 0. The total variation topology can only see that $\delta_{0.0000001}$ and $\delta_0$ are assigning mass to disjoint sets; it can't see that those sets are very close together in the topology of $\mathbb{R}$. But the weak topology can. $\endgroup$ – Nate Eldredge May 16 '17 at 15:45
  • $\begingroup$ I see, thanks for the clear example. The total variation is infamously brittle... (I basically only use it in discrete settings, where this is less of an issue.) But that is not the case for, say, Wasserstein, right? $\endgroup$ – Clement C. May 16 '17 at 19:26
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    $\begingroup$ @ClementC.: Right, the Wasserstein distance typically induces the weak topology. $\endgroup$ – Nate Eldredge May 16 '17 at 19:27

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