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Econ-286 · economics · Economics

Information and statistical power under interference

LLM status Open unverified

Statement

CI-14 |  Econometric theory and causal inference

Research agenda formalization

Definitions and assumptions

For a graph $G_N$, fix an exposure vector $S(W)$, pretreatment design matrix $X$, and allowable randomized-assignment designs $\mathcal E$ with a specified treatment budget. An explicit reference model is \[ Y=X\gamma+\beta_{\rm dir}W+\delta S(W)+\epsilon,\qquad \epsilon\sim N(0,\Sigma),\quad \epsilon\perp W. \] The covariance $\Sigma$ is symmetric positive definite. This Gaussian subclass permits unbounded outcomes. The equations define causal potential outcomes by substituting each assignment vector; $\delta$ is the common spillover coefficient. Let $A=[X,W]$, and let $\Pi_{\Sigma^{-1/2}A}$ be the orthogonal projection onto its column space. Conditional efficient information is \[ I_\delta(W,\Sigma)= S(W)^\top\Sigma^{-1/2} (I-\Pi_{\Sigma^{-1/2}A})\Sigma^{-1/2}S(W). \] Relative to a declared independent-unit reference information $v_{\rm ref}/\sigma_{\rm ref}^2>0$, define $n_{\rm eff}(q,\Sigma,S)=\sigma_{\rm ref}^2\E_q[I_\delta]/v_{\rm ref}$.

Formal question

For a specified positive-definite covariance class $\mathcal S$ and $\alpha,\beta\in(0,1)$ with $\alpha+\beta<1$, determine the minimax separation for testing $\delta=0$ versus $|\delta|\geq\Delta_N$ with type-I error at most $\alpha$ and worst-case power at least $1-\beta$. Optimize this tradeoff over $q\in\mathcal E$, and derive matching limits when exposure is nearly collinear with own treatment or nuisance covariates. Characterize how the results change when $\Sigma$ must be estimated from a declared number of independent networks.

Scope and status

The known-covariance Gaussian information calculation is a reference subproblem, not an unsolved theorem. The agenda refinement concerns robust power and design limits. Effective replication depends on the target exposure, assignment design, nuisance structure, and dependence; no scalar depending only on the graph is asserted to characterize every interference problem. Designs with zero residual exposure information cannot yield nontrivial uniform power.

References for the source of the problem

Carlos Cinelli, Avi Feller, Guido Imbens, Edward Kennedy, Sara Magliacane, and Jose Zubizarreta, Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference, arXiv:2508.17099v1, 23 August 2025, Section 3.2.3, paragraph Statistical power. https://arxiv.org/html/2508.17099v1.

Common conventions: Source mathematical conventions

Well-defined objects. All probability spaces and measurable variables are specified or inherited from a local statistical model. Expectations used as finite targets require the stated integrability. A decision rule or estimator is measurable with respect to its available information; randomized rules may use an auxiliary independent random variable. Norms without a subscript are Euclidean in finite-dimensional spaces. An optimizer is requested only when attainment is assured; otherwise the question concerns the optimal value and $\varepsilon$-optimal admissible rules for every $\varepsilon>0$.

Identification. A structural model $m\in\mathcal M$ implies an observable law $P_m^{\rm obs}$ and target $\theta(m)$. The target is point identified on $\mathcal M$ if \[ P_{m_1}^{\rm obs}=P_{m_2}^{\rm obs} \quad\Longrightarrow\quad\theta(m_1)=\theta(m_2) \qquad(m_1,m_2\in\mathcal M). \] When this fails, the sharp identified set is \[ \Theta(P;\mathcal M)=\{\theta(m):m\in\mathcal M, P_m^{\rm obs}=P\}. \] Specifying an intervention or estimand does not establish identification. Positivity, exclusion, causal sufficiency, measurement restrictions, and transport assumptions are substantive local inputs, never automatic consequences of notation.

Minimax risk and nontrivial inference. For a statistical experiment with data law $P^{(n)}$, target $\theta(P)$, and nonnegative loss $\ell$, define \[ \mathcal R_n(\mathcal P,\ell) =\inf_{\widehat\theta_n}\sup_{P\in\mathcal P} \E_{P^{(n)}}\ell(\widehat\theta_n,\theta(P)). \] The infimum is over measurable estimators. A sharp rate is a sequence $r_n$ for which $0<c\leq C<\infty$, independent of $n$, satisfies $c r_n\leq\mathcal R_n\leq C r_n$ for all sufficiently large $n$. Squared-error parametric risk is of order $n^{-1}$; the corresponding root-mean-squared error is of order $n^{-1/2}$. These different conventions are distinguished locally. A uniform asymptotic confidence region $C_n$ must satisfy \[ \liminf_{n\to\infty}\inf_{P\in\mathcal P} \Pr_{P^{(n)}}\{\theta(P)\in C_n\}\geq 1-\alpha, \qquad 0<\alpha<1, \] together with an informative diameter or expected-width requirement. Coverage alone permits the vacuous whole parameter space. Testing questions similarly impose both size control and power against a declared separated alternative class.

Binary causal baseline. When an entry explicitly invokes this baseline, one observes independent copies of $O=(X,W,Y)$, with $W\in\{0,1\}$ and potential outcomes $Y(0),Y(1)$. Assume consistency $Y=Y(W)$, no interference, conditional exchangeability $(Y(0),Y(1))\perp W\mid X$, and overlap $\epsilon\leq e(X)\leq1-\epsilon$ almost surely for a fixed $0<\epsilon<1/2$, where \[ e(x)=\Pr(W=1\mid X=x),\qquad m_w(x)=\E[Y\mid W=w,X=x],\qquad \tau(x)=m_1(x)-m_0(x). \] These assumptions identify conditional mean effects almost everywhere under the covariate law. Pointwise or uniform effects require appropriate regularity and a specified support. Continuous treatment, longitudinal data, adaptive experiments, and network interference require their own assumptions in place of this baseline. Bounded outcomes, densities, and smoothness are additional assumptions only where stated.

Function classes. On $[0,1]^d$, $\mathcal H_d^s(L)$ is the isotropic Hölder ball of radius $L>0$: put $k=\lceil s\rceil-1$ and $\gamma=s-k\in(0,1]$, bound derivatives through order $k$, and require the order-$k$ derivatives to be $\gamma$-Hölder with constant at most $L$. Thus integer orders use a Lipschitz final derivative convention. The class $\operatorname{BV}_d(L)$ consists of functions $f\in L^1((0,1)^d)$ whose distributional derivative is a finite vector measure and for which $\|f\|_{L^1}+|Df|((0,1)^d)\leq L$. An $L^\infty$ bound or a particular pointwise version is imposed separately when needed. Sparse and neural-network classes require a declared dictionary or architecture and coefficient bounds.

An anisotropic Hölder class with declared block smoothness indices means coordinatewise Hölder regularity in each block, uniformly in the other blocks, with all corresponding derivative and Hölder bounds at most the stated radius. Derivative orders and the integer-order convention in each block are those just given. Mixed-derivative bounds are additional restrictions only when explicitly stated.

Decision and computational questions. Policy regret compares admissible feasible policies under the same law and constraints. In computational entries, finite data and thresholds have rational encodings; running time is measured in their total bit length. Real-valued equilibrium search uses the explicitly declared algebraic or FIXP encoding. An approximation ratio for maximization is written $\operatorname{OPT}/\operatorname{ALG}$ and for minimization as $\operatorname{ALG}/\operatorname{OPT}$, with zero optima and infeasible instances handled locally. Historical approximation bounds are not automatically current bounds.

Statement licence: CC-BY-4.0 · Source file

Contributions 1

Econ-286-A1 · Exploration · LLM-generated · gpt 6 astra ultra ·

GPT 6 Astra Ultra attempt on Econ-286

Information and statistical power under interference Robust conditional tests and exact designs on paired networks

Question and scope

The target is design-dependent power for spillover effects when exposure is correlated with direct treatment and errors may be dependent. The known Gaussian information formula alone is not a solution. This attempt gives a covariance-robust conditional test, a matching small-information obstruction, and a complete optimal-design calculation on disjoint pairs. It also distinguishes conditional collinearity from unconditional identification under randomized treatment counts.

Fixed-design robust inference

There are $N$ vertices with a fixed observed graph. Treatment $w\in\{0,1\}^N$ determines exposure $s=S(w)\in{\mathbb{R}}^N$. Conditional on this assignment, \[ Y=A\theta+\delta s+\varepsilon,\qquad A=[X,w],\quad\varepsilon\sim N(0,\Sigma), \] where $X$ contains recorded pretreatment regressors, $\theta$ is unrestricted and $\Sigma$ is independent of treatment. Let $\Pi_A$ be the Euclidean orthogonal projection onto the column space of $A$, even if $A$ is rank deficient, and set $r=(I-\Pi_A)s$. The covariance class is $\mathcal S=\{\Sigma:0<\underline v I\preceq\Sigma\preceq\bar v I\}$ with known $0<\underline v\le\bar v<\infty$. Denote the standard normal distribution function by $\Phi$ and its $p$ quantile by $z_p$.
Theorem (Uniform conditional size and power): For $r\ne0$, reject $\delta=0$ when \[ |r'Y|>z_{1-\alpha/2}\sqrt{\bar v}\,\|r\|, \qquad 0<\alpha<1. \] For $r=0$, never reject. The test has size at most $\alpha$ uniformly over $\theta$ and $\Sigma\in\mathcal S$, conditional on every assignment. For $0<\beta<1/2$, its power is at least $1-\beta$ whenever \[ |\delta|\|r\|\ge\sqrt{\bar v} (z_{1-\alpha/2}+z_{1-\beta}). \] For this covariance class the worst-case efficient conditional information is exactly $\|r\|^2/\bar v$.
Proof: Because $r'A=0$ and $r's=\|r\|^2$, the contrast $r'Y$ is normal with mean $\delta\|r\|^2$ and variance $r'\Sigma r\le\bar v\|r\|^2$. A centered normal with smaller variance has smaller two-sided tails at a positive fixed cutoff, proving the size bound. Under a positive mean, the probability of falling below the positive cutoff is at most $\beta$ if the distance from the mean to that cutoff is at least $z_{1-\beta}\sqrt{\bar v}\|r\|$; a negative mean is symmetric. This gives the power bound.

Efficient information is the minimized weighted squared distance $\min_b(s-Ab)'\Sigma^{-1}(s-Ab)$. Since $\Sigma\preceq\bar v I$, it is at least $\min_b\|s-Ab\|^2/\bar v=\|r\|^2/\bar v$. Equality holds for the admissible covariance $\Sigma=\bar v I$. ∎

Randomizing $w$ independently of errors preserves size by conditioning and averaging. A uniform conditional power guarantee requires the residual norm bound on every supported assignment; maximizing its expectation alone cannot give such a guarantee.

Theorem (Exact two-point obstruction): At a fixed assignment and $\Sigma=\bar v I$, for any $\Delta>0$ there are nuisance values under $\delta=0$ and $\delta=\Delta$ such that the sum of errors of every test is at least \[ 2\Phi\left(-\frac{\Delta\|r\|}{2\sqrt{\bar v}}\right). \] If $r=0$, the two observation laws can be made identical for every $\Delta$, so no test has size $\alpha$ and power greater than $\alpha$ uniformly over nuisance values.
Proof: Choose $b$ with $Ab=\Pi_A s$. Under the null take nuisance $\theta_0=0$; under the alternative take $\theta_1=-\Delta b$. Then the means are zero and $\Delta r$. Their Gaussian likelihood ratio exceeds one exactly when $r'Y>\Delta\|r\|^2/2$. At each observation, assigning it to the larger density minimizes the sum of the two error probabilities, since the error contribution is then the smaller density. For this likelihood-ratio rule both errors equal $\Phi(-\Delta\|r\|/(2\sqrt{\bar v}))$. No other test can improve their sum. If $r=0$, the means and covariances coincide. ∎

Thus the separation scale $\sqrt{\bar v}/\|r\|$ is unavoidable, up to error-probability constants. It is a feature of the design and exposure relation, not simply graph size or average degree.

A complete design calculation on disjoint pairs

Let $N=2n$ and let the graph consist of $n$ disjoint edges. Exposure is the treatment of the unique neighbor. Let $X=\mathbf1$ and impose exactly $n$ treated vertices. Write $m$ for the number of mixed edges (one treated endpoint). The other edges are equally divided between types $00$ and $11$, so $0\le m\le n$ and $n-m$ is even.
Theorem (Paired-network optimality): For every such assignment, \[ \|(I-\Pi_{[\mathbf1,w]})S(w)\|^2 =2m\left(1-\frac mn\right). \] Consequently the assignments maximizing worst-case conditional information are exactly those whose feasible integer $m$ is closest to $n/2$. If $N$ is a multiple of eight, the optimum is $m=n/2$ and the residual norm squared is $N/4$. Both pure cluster assignment ($m=0$) and exactly one treated vertex per edge ($m=n$) have zero information. A randomized design supported entirely on the optimal assignments achieves the same information guarantee while retaining random treatment assignment.
Proof: Both $w$ and $s=S(w)$ have exactly $n$ ones, so their centered versions have squared norm $N/4=n/2$. Each mixed edge contributes $-1/2$ to their inner product and each unmixed edge contributes $1/2$. Thus their inner product is $(n-2m)/2$, and their correlation is $1-2m/n$. Regressing centered $s$ on centered $w$ leaves squared residual \[ \frac n2\left[1-\left(1-\frac{2m}n\right)^2\right] =2m(1-m/n). \] This concave quadratic is maximized by feasible integers nearest its vertex $n/2$. When $N=8k$, $n=4k$ and $m=2k$ is feasible because $n-m=2k$ is even. Substitution gives $n/2=N/4$. The zero-information endpoints follow directly. Since all assignments in the proposed randomization have the same residual norm, the conditional information and test guarantees hold for every draw. ∎

A concrete optimal design selects $n/4$ edges to be $00$, $n/4$ to be $11$ and $n/2$ to be mixed, randomizing these labels uniformly and the treated endpoint of every mixed edge fairly. Every vertex then has marginal treatment probability $1/2$. It satisfies both the exact budget and the information optimum; correlation across assignments is part of the design, not a violation of randomization.

Complete graphs and the random-count qualification

Proposition (A global nonidentification example): On the complete graph with $N\ge2$, define neighbor-average exposure $S_i(w)=(\sum_jw_j-w_i)/(N-1)$. With an intercept and exactly $k$ treated vertices in every supported assignment, the joint law of $(W,Y)$ depends on $(\gamma,\beta,\delta)$ only through \[ \gamma+\delta k/(N-1),\qquad \beta-\delta/(N-1), \] where $\gamma$ is the intercept and $\beta$ the direct effect. Thus $\delta$ is not identified even unconditionally. If the design instead supports two different treatment counts and contains nonconstant assignments at those counts, conditional collinearity by itself no longer proves this nonidentification.
Proof: For fixed count $k$, $S(w)=k\mathbf1/(N-1)-w/(N-1)$ with the same coefficients for every assignment. Substitution gives the stated two combinations, so changing $\delta$ and offsetting $\gamma,\beta$ produces identical joint laws.

For varying counts, suppose two parameter triples have identical conditional means for every supported assignment. On a nonconstant assignment, the coefficients on $\mathbf1$ and $w$ must separately agree, giving $\Delta\beta=\Delta\delta/(N-1)$ and $\Delta\gamma=-\Delta\delta k/(N-1)$. Two different counts force $\Delta\delta=0$. Thus in this support configuration varying treatment counts removes this mean-level aliasing, even though each individual assignment has zero residual exposure after fitting its own intercept and direct effect. ∎

Independent network replicates and the remaining problem

If the same realized assignment (and therefore the same contrast $r$) is implemented on $M$ independent networks with the same $(\theta,\delta,\Sigma)$, the scalar contrasts $r'Y_j/\|r\|^2$ are independent normal observations with mean $\delta$ and variance $r'\Sigma r/\|r\|^4$. Their average has variance divided by $M$, giving the preceding known-bound test with separation divided by $\sqrt M$. This conclusion follows by multiplying the normal moment-generating functions. Dependence between those networks would invalidate that replicate count; independence must concern the errors and the intervention system, not merely labels assigned to graph pieces.

For general graphs, finding the best residual geometry subject to treatment constraints is a nontrivial combinatorial problem. The spectral covariance bound used here is deliberately conservative and does not solve power optimization under rich estimated covariance classes. Optimal unconditional tests with fixed nuisance across assignments can exploit information discarded by conditional projection, as the complete-graph example shows. These general questions remain unresolved.

The primary survey text's Section 3.2.3 was checked for its statistical-power and replicates discussion. The paired-graph optimum and covariance-class specialization above are deductions for this explicitly stated model, not claimed solutions attributed to the survey.

Completion Estimate

49%

This is a post hoc, heuristic estimate for the full catalogue target. The attempt proves covariance-robust conditional tests and a matching information-scale obstruction, and exactly optimizes assignment on disjoint pairs. General graph design, optimal unconditional testing, and informative power guarantees with estimated covariance from network replicates remain unresolved.

References

  • [c] C. Cinelli, A. Feller, G. Imbens, E. Kennedy, S. Magliacane and J. Zubizarreta (2025), Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference, arXiv:2508.17099v1, Section 3.2.3. https://arxiv.org/html/2508.17099v1.

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