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Under a product measure on a finite Boolean lattice, an increasing event $E$ and a decreasing event $G$ obey the displayed inequality. The complement $G^c$ is an increasing event, so Harris' inequality gives $\mathbb{P}(E\cap G^c)\ge \mathbb{P}(E)\mathbb{P}(G^c)$. Subtract from $\mathbb{P}(E)$ to obtain the result. This implication includes degenerate Bernoulli distribution parameters $0$ and $1$.

The printed inclusion symbol here means non-strict inclusion: the PDF explicitly excludes a common member of the two set families. We use $\subseteq$ throughout. Thus a cross-Sperner family pair need not consist of internal antichains; the restriction is between the two families.

Give the Boolean lattice the uniform probability measure, and write $N=2^n$. Form the upward closure of a set family and downward closure of a set family of $\mathcal{A}$:These are respectively an increasing event and a decreasing event. We have $\mathcal{A}\subseteq U\cap D$, whereas the cross-Sperner family condition gives $\mathcal{B}\subseteq U^c\cap D^c$. Set $u=\mathbb{P}(U)$ and $d=\mathbb{P}(D)$. By negative correlation of increasing and decreasing events, first for $U,D$ and then for $U^c,D^c$,Apply the Cauchy-Schwarz inequality to the unit vectors $(\sqrt{u},\sqrt{1-u})$ and $(\sqrt{d},\sqrt{1-d})$. It yieldsTherefore the Cross-Sperner inequality isIf the printed inclusion symbol were read as strict inclusion while the explicit exclusion of common members were discarded, this conclusion would fail, for example with both families equal to the middle uniform set family when $n=2$. The PDF's parenthetical clause fixes the intended convention.

Harris' inequality states that, for a product measure on $\{0,1{\}}^n$ with independent Bernoulli random variables of parameters $p_i\in [0,1]$, increasing events $E,F$ satisfyIn particular, the uniform probability measure on the Boolean lattice gives $|E\cap F|\ge |E||F|/2^n$. We give a direct mathematical induction proof, so no stronger correlation theorem is assumed.

Prove more generally that $\mathbb{E}[fg]\ge \mathbb{E}[f]\mathbb{E}[g]$ for real coordinatewise increasing functions $f,g$ on the finite cube. For $n=0$ both are constant and equality holds. For the inductive step let $p=p_n$, and let $f_j,g_j$ be the restrictions obtained by setting the last coordinate equal to $j\in \{0,1\}$. Write $a_j=\mathbb{E}[f_j]$, $b_j=\mathbb{E}[g_j]$, using the product measure on the other coordinates. Monotonicity gives $a_0\le a_1$, $b_0\le b_1$. By the inductive hypothesis and conditional expectation,The nonnegative correction term also handles $p=0$ and $p=1$. Taking indicator functions $f=1_E$, $g=1_F$ proves Harris' inequality.

For later use, an increasing event $E$ and a decreasing event $G$ have negative correlation of increasing and decreasing events. The complement $G^c$ is an increasing event, so

For bond percolation, the Harris-FKG inequality states that any two increasing events $A,B$ satisfyThe same inequality holds for two decreasing events, either by reversing the coordinate order or by taking complements in the two-event identity. Intersections of decreasing events are decreasing events, so induction givesWrite $a={\mathbb{P}}_p(A_1)=\cdots ={\mathbb{P}}_p(A_n)$ and $u={\mathbb{P}}_p({\bigcup }_j{A}_j)$. Then $1-u\ge (1-a{)}^n$. Taking the nonnegative $n$-th root and rearranging yields the square-root trick for positively associated events:No independence among the events is needed.