Each result cited is universally quantified over the data in its own statement. Throughout, U(X)=u(L(X)) for every X∈L2(Ω;Rd) by The Lift of a Function on the Wasserstein Space to the Space of Square-Integrable Random Vectors §lift, and L(X)∈P2(Rd) by The Wasserstein Space and Its Lift to Square-Integrable Random Vectors: Standing Notation §law-map. The metric on R is dR(s,t)=∣s−t∣ (Real Hilbert Spaces: Standing Notation and Background §numbers), so that for real-valued functions the distance between two values is the absolute value of their difference; the metric on L2(Ω;Rd) is dL2(X,Y)=∥X−Y∥L2 (The Wasserstein Space and Its Lift to Square-Integrable Random Vectors: Standing Notation §space) and that on P2(Rd) is W2 (The Wasserstein Space and Its Lift to Square-Integrable Random Vectors: Standing Notation §wasserstein). Two facts about the law map are used repeatedly, both from The Wasserstein Space and Its Lift to Square-Integrable Random Vectors: Standing Notation §law-map: the domination inequality of The Wasserstein Distance and the Mean-Square Distance of Random Vectors §inequality, namely, for all X,Y∈L2(Ω;Rd),
W2(L(X),L(Y))≤∥X−Y∥L2,
and, when (Ω,F,P) is rich, every μ∈P2(Rd) is the law of some X∈L2(Ω;Rd) (The Wasserstein Distance and the Mean-Square Distance of Random Vectors §onto), and for all μ,ν∈P2(Rd) the set D(μ,ν)={∥X−Y∥L2:L(X)=μ, L(Y)=ν} (the letter D denoting this set and not a gradient throughout this proof) is nonempty and bounded below with infD(μ,ν)=W2(μ,ν) (The Wasserstein Distance and the Mean-Square Distance of Random Vectors §infimum), the infimum being the greatest lower bound.
Claim 1. Let X,Y∈L2(Ω;Rd) with L(X)=L(Y). Then U(X)=u(L(X))=u(L(Y))=U(Y), so U is law-invariant in the sense of Law-Invariant Function on the Space of Square-Integrable Random Vectors §invariant.
Claim 2. Let (Ω,F,P) be rich and let Φ be law-invariant. Existence. For μ∈P2(Rd) the set {Φ(X):X∈L2(Ω;Rd), L(X)=μ} is nonempty by The Wasserstein Distance and the Mean-Square Distance of Random Vectors §onto and has exactly one element, since Φ(X)=Φ(X′) whenever L(X)=μ=L(X′) by Law-Invariant Function on the Space of Square-Integrable Random Vectors §invariant; let v(μ) be that element. This defines v:P2(Rd)→R, and for every X∈L2(Ω;Rd) the element X itself has law L(X), so v(L(X))=Φ(X); that is, the lift of v is Φ. Uniqueness. Let v,v′:P2(Rd)→R both have lift Φ and let μ∈P2(Rd). Choose X with L(X)=μ by The Wasserstein Distance and the Mean-Square Distance of Random Vectors §onto; then v(μ)=v(L(X))=Φ(X)=v′(L(X))=v′(μ). Hence v=v′.
Claim 3. Let u be bounded with bound M, so that ∣u(μ)∣≤M for every μ∈P2(Rd) by Bounded Real-Valued Function on a Set. For X∈L2(Ω;Rd), ∣U(X)∣=∣u(L(X))∣≤M; so U is bounded with bound M.
Claim 4. Let (Ω,F,P) be rich and U bounded with bound M. For μ∈P2(Rd) choose X with L(X)=μ by The Wasserstein Distance and the Mean-Square Distance of Random Vectors §onto; then ∣u(μ)∣=∣U(X)∣≤M. So u is bounded with bound M.
Claim 5. Let u be Lipschitz with constant κ, so that ∣u(μ)−u(ν)∣≤κW2(μ,ν) for all μ,ν∈P2(Rd) by Lipschitz Map Between Metric Spaces. For X,Y∈L2(Ω;Rd),
∣U(X)−U(Y)∣=∣u(L(X))−u(L(Y))∣≤κW2(L(X),L(Y))≤κ∥X−Y∥L2,
the last step by the domination inequality and claim 5 of Elementary Arithmetic in an Ordered Field (multiplication by the nonnegative κ), and the two inequalities combine by the transitivity of ≤, the order of R being that of an ordered field (Real Hilbert Spaces: Standing Notation and Background §numbers). So U is Lipschitz with constant κ.
Claim 6. Let (Ω,F,P) be rich and U Lipschitz with constant κ, and let μ,ν∈P2(Rd); put c=∣u(μ)−u(ν)∣. For every s∈D(μ,ν), say s=∥X−Y∥L2 with L(X)=μ and L(Y)=ν, we have c=∣U(X)−U(Y)∣≤κs by Lipschitz Map Between Metric Spaces. If κ=0, choose s∈D(μ,ν), which is nonempty; then c≤κs=0=κW2(μ,ν) by claim 1 of Zero Products and Elementary Identities in a Field. If κ=0, then κ−1 exists, and 0≤κ−1 by claim 4 of Elementary Arithmetic in an Ordered Field; multiplying c≤κs by κ−1 (claim 5 there) gives κ−1c≤κ−1(κs)=s for every s∈D(μ,ν), so κ−1c is a lower bound of D(μ,ν) and hence κ−1c≤infD(μ,ν)=W2(μ,ν) by Lower Bound and Greatest Lower Bound; multiplying by the nonnegative κ (claim 5 again) gives c=κ(κ−1c)≤κW2(μ,ν). In both cases ∣u(μ)−u(ν)∣≤κW2(μ,ν), so u is Lipschitz with constant κ.
Claim 7. Let u be uniformly continuous and let ε>0. By Uniformly Continuous Map Between Metric Spaces there is δ>0 such that ∣u(μ)−u(ν)∣<ε whenever W2(μ,ν)<δ. Let X,Y∈L2(Ω;Rd) with ∥X−Y∥L2<δ. Then W2(L(X),L(Y))<δ by the domination inequality and claim 2 of Elementary Order Arithmetic in an Ordered Field, hence ∣U(X)−U(Y)∣=∣u(L(X))−u(L(Y))∣<ε. So U is uniformly continuous.
Claim 8. Let (Ω,F,P) be rich and U uniformly continuous, and let ε>0; choose δ>0 with ∣U(X)−U(Y)∣<ε whenever ∥X−Y∥L2<δ. Let μ,ν∈P2(Rd) with W2(μ,ν)<δ. Since D(μ,ν) is nonempty and bounded below with infD(μ,ν)=W2(μ,ν)<δ, claim 2 of Approximation Property of the Supremum and the Infimum in R gives s∈D(μ,ν) with s<δ, say s=∥X−Y∥L2 with L(X)=μ and L(Y)=ν. Then ∣u(μ)−u(ν)∣=∣U(X)−U(Y)∣<ε. So u is uniformly continuous.
Claim 9. Let u be continuous on P2(Rd), let X∈L2(Ω;Rd) and let ε>0. By Continuous Map Between Metric Spaces applied at L(X) there is δ>0 such that every ν∈P2(Rd) with W2(L(X),ν)<δ satisfies ∣u(ν)−u(L(X))∣<ε. Let Y∈L2(Ω;Rd) with ∥X−Y∥L2<δ. Then W2(L(X),L(Y))<δ by the domination inequality and claim 2 of Elementary Order Arithmetic in an Ordered Field, so ∣U(Y)−U(X)∣=∣u(L(Y))−u(L(X))∣<ε. Hence U is continuous at X relative to L2(Ω;Rd), and, X being arbitrary, continuous on L2(Ω;Rd).