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Cyrus Samii is a political scientist at New York University whose methodological work concerns quantitative field research and causal inference. His academic biography connects the design of randomized controlled trials to empirical social research.^[ref]

With Peter M. Aronow, Samii studied sampling designs in which every unit can be observed but some pairs can never be observed together. The distinction matters: positive individual inclusion probabilities suffice for an unbiased estimator of a finite population total, whereas the usual Horvitz-Thompson variance estimator also needs pairwise inclusion positivity.^[ref]

An original two-unit example makes the single-draw variance identifiability obstruction explicit. Fix two unknown population values $y_1,y_2$ and choose one index $J$ with probabilities $1/2,1/2$. The Horvitz-Thompson estimator is $\hat{T}=2y_J$, soAn estimator based only on the selected index and its observed value has expected value of the form $[a(y_1)+b(y_2)]/2$. Such an additive expression cannot equal $(y_1-y_2{)}^2$ for every pair of real values. Taking the mixed finite difference at $(0,0),(1,0),(0,1),(1,1)$ gives zero for the additive expression butfor the desired variance. Thus a single draw permits an unbiased estimator of the total but no universally unbiased estimator of its sampling variance. Independent auxiliary randomization cannot repair this: averaging that randomness still gives functions $a$ and $b$ of the same observed data.

This is a restriction imposed by the observation mechanism, rather than a shortage of algebraic ingenuity. It motivates separating design-based inference about a total from the additional information needed to quantify its uncertainty.

Design-based inference evaluates an estimator by the random mechanism selecting a sample from a fixed finite population. Population values $y_1,\dots ,y_N$ are held fixed; the selected subset $S$ is random under a known sampling design $p(s)$. ThusThe subscript $p$ identifies the source of randomness. An unbiased estimator of a finite population total has ${\mathbb{E}}_p\hat{T}=T$ for each eligible fixed population. The unequal-probability total developed by D. G. Horvitz and D. J. Thompson is a central example.^[ref]

For an original example, let the fixed population values be $(1,1,10)$ and sample one label uniformly. Every inclusion probability is $1/3$, so the Horvitz-Thompson estimator is $3y_i$. Its three equally probable values are $3,3,30$, while the actual total is $12$. Consequently,The estimator is unbiased even though none of its possible estimates equals the target. Unbiasedness describes averaging across the sampling design, rather than guaranteed accuracy in an individual sample.

A confidence interval with design-based coverage must likewise succeed at its stated rate across repeated selections under the specified design. That property requires a justified construction; unbiasedness of a point estimate alone supplies no coverage guarantee. Repeating a convenient calculation on the same selected units does not reproduce the selection process. Assumptions about measurement error or missing responses must also be stated separately, since knowledge of the original selection probabilities alone does not repair those later stages.

D. G. Horvitz coauthored the 1952 paper that generalized finite-population sampling without replacement to unequal selection probabilities. The joint work with D. J. Thompson provides the foundation for the Horvitz-Thompson estimator.^[ref]

The central idea is to compensate for unequal chances of inclusion. For fixed population values $y_i$ and positive inclusion probabilities ${\pi }_i$, write a total estimate asUsing an inclusion indicator $I_i$ gives $\mathbb{E}(I_i/{\pi }_i)=1$, so $\mathbb{E}\hat{Y}={\sum }_i{y}_i$. Unbiasedness follows from the design probabilities rather than from assuming that the sampled values are an independent realization of a population model.^[ref]

For an illustrative population $(4,6)$, select exactly one unit with probabilities $(1/4,3/4)$. The total estimate is $16$ or $8$, respectively, with expected value $(1/4)16+(3/4)8=10$, the true total. The less frequently selected unit receives the larger multiplier. This example explains how a sampling design can deliberately favor some units without forcing an unweighted analysis to be correct.

D. J. Thompson coauthored the 1952 general treatment of sampling without replacement from a finite population with unequal selection probabilities. The paper with D. G. Horvitz is the source of the Horvitz-Thompson estimator.^[ref]

The contribution separates unbiased total estimation from assumptions of independent selection. If $I_i$ is an inclusion indicator, then $\mathbb{E}{I}_i={\pi }_i$, while pair products satisfy $\mathbb{E}(I_i{I}_j)={\pi }_{ij}$. Consequently, for fixed population values,This identity makes joint inclusion probabilities essential to a variance calculation, even though the expectation of the total estimate only needs the individual inclusion probabilities.

For an illustrative population $(1,1)$, sample one unit uniformly. Each selected value is multiplied by $2$, so the total estimate is always $2$ and its variance is zero. The individual random contributions are nevertheless variable: their covariance cancels their variances because they cannot both be selected. Ignoring that sampling dependence would give the wrong precision. The example shows why design-based estimation concerns the whole selection mechanism, not just a set of multipliers.

A finite population total adds a specified numerical value over every labelled unit in a fixed, finite population $U=\{1,\dots ,N\}$:The target must specify both the population and the measured variable. It may be a total inventory mass, the total number of employees across establishments, or the sum of a binary property. If $y_i\in \{0,1\}$, the total counts units having the property; it is not automatically the population size $N$. A finite population mean is $\overline{Y}=T/N$, when $N$ is known and positive.

An original inventory has four containers holding $3,7,10,20\mathrm{kg}$ of a material. The total is $40\mathrm{kg}$ and the finite population mean is $10\mathrm{kg}$ per container. Giving every container the same mass fraction of an analyte is a separate assumption: the total analyte mass instead requires summing each container's mass multiplied by its own mass fraction. The target follows the physical quantity being added.

When only a sample $S$ is observed, the unweighted sample sum is generally not an unbiased estimator of $T$. Using inclusion indicators $I_i$ and inclusion probabilities ${\pi }_i$ givesIf every ${\pi }_i>0$, the Horvitz-Thompson estimator instead satisfiesThis is a consequence of the sampling design, without requiring the fixed population values to follow a statistical distribution.^[ref] A full census removes sample-selection variation, but imperfect measurement can still make the recorded sum differ from the intended physical total.

The Horvitz-Thompson variance estimator estimates the sampling variance of a Horvitz-Thompson estimator using observed values and known individual and joint inclusion probabilities. It is a random estimate of a fixed design-dependent variance, rather than the variance itself.

Let $I_i$ be an inclusion indicator, ${\pi }_i={\mathbb{E}}_p{I}_i>0$, and ${\pi }_{ij}={\mathbb{E}}_p(I_i{I}_j)$, with ${\pi }_{ii}={\pi }_i$. For fixed population values $y_i$, write $a_i=y_i/{\pi }_i$. Bilinearity of covariance givesUnder pairwise inclusion positivity, a sample $S$ provides the estimatorThe diagonal terms are included. Taking expected values replaces $I_i{I}_j/{\pi }_{ij}$ by 1, proving ${\mathbb{E}}_p{\hat{V}}_{\mathrm{HT}}=V_p$. This is the pairwise counterpart of inverse-probability expansion for the finite population total.^[ref]

An original exact example uses fixed values $(3,0,1)$. Select $\{1,2\},\{1,3\},\{2,3\}$ with probabilities $1/2,1/4,1/4$, respectively. Then $({\pi }_1,{\pi }_2,{\pi }_3)=(3/4,3/4,1/2)$, and the possible total estimates are $4,6,2$. Their expected value is the true total 4, with varianceFor those same samples, the variance estimates are $4,-2,2$. Their expected value is also 2. In particular, for $S=\{1,3\}$, the two diagonal contributions are 4 and 2, while the combined off-diagonal contribution is $-8$, giving $-2$.

Thus this unbiased estimator of variance can be negative in a realized sample. Replacing a negative result by zero changes its expected value; in this example that modification raises the mean estimate to $5/2$. A positive square root is therefore not always available as a sample standard error from this particular formula. If some ${\pi }_{ij}=0$, dropping an unobservable cross-product does not preserve the proof; the missing term needs separate analysis.

Pairwise inclusion positivity requires every distinct pair of population units to have positive joint inclusion probability under the sampling design:For Horvitz-Thompson variance estimator, the probabilities must also be known, and individual inclusion probabilities must be positive. Each pair can then, at least in principle, contribute an observed product to a pair-expansion estimate. Positivity is not independence: ${\pi }_{ij}$ need not equal ${\pi }_i{\pi }_j$.^[ref]

For simple random sampling of $n$ distinct labels from $N\ge 2$, ${\pi }_{ij}=n(n-1)/\{N(N-1)\}$. Consequently, $n\ge 2$ gives pairwise positivity, whereas selecting exactly one label gives positive individual inclusion probabilities but zero probability of ever observing two distinct values together. This distinction separates unbiased total estimation from unbiased estimation of its sampling variance.

An original two-unit proof shows what information is missing. Sample one of two fixed values $y_1,y_2$ uniformly. The Horvitz-Thompson estimator is $2y_1$ or $2y_2$, and its variance isAny estimator based only on the observed label and value has expected value of the form $\{g_1(y_1)+g_2(y_2)\}/2$. Its mixed difference over $(y_1,y_2)\in \{0,1{\}}^2$ is zero. The target's mixed difference is insteadNo such estimator can therefore be unbiased for this variance for every possible pair of values. Additional randomization internal to the estimator does not help: conditioning on the observed data still produces functions of this same separated form. This is an information limitation, not division by zero that can be repaired by assigning a convenient numerical convention.^[ref]

Strict positivity alone also does not guarantee useful precision. A very small ${\pi }_{ij}$ produces a large inverse-probability multiplier on a rare observed pair. Designing a usable survey therefore requires attention to both observability and the resulting sampling variance.

Peter M. Aronow develops statistical methods for causal inference, particularly design-based inference. Yale's institutional biography identifies research on complex experimental designs, network settings and incomplete survey data alongside appointments spanning political science and statistics.^[ref]

His joint work with Cyrus Samii studies how to estimate sampling variance when a sampling design never observes some pairs together. A missing product cannot simply be deleted from the population variance formula: doing so can produce either sign of bias. Their construction uses Young inequality to bound unobserved products by individually estimable powers and obtain a conservative variance estimator.^[ref]

For an original exact illustration, choose one of two fixed values $y_1,y_2$ with equal probabilities. As in the single-draw variance identifiability obstruction, the Horvitz-Thompson estimator $2y_J$ has true variance $V_p=(y_1-y_2{)}^2$. Retaining only diagonal contributions gives $D=2y_J^2$, withOpposite signs therefore produce downward bias. The elementary inequality $-2ab\le a^2+b^2$, obtained from $(a+b{)}^2\ge 0$, instead givesConsequently $C=4y_J^2$ is a conservative variance estimator for this stipulated sampling design; its excess in expected value is exactly $(y_1+y_2{)}^2$.

With $(y_1,y_2)=(3,-1)$, the true variance is16. The two possible values of $D$ are18 and2, averaging10; those of $C$ are36 and4, averaging20. Although ${\mathbb{E}}_{p}C\ge 16$, a realized value of $C$ can be below16. Conservativeness here is an expectation inequality, rather than a bound for every sample. By itself it also does not prove a specified confidence interval coverage probability.

A sampling design specifies a probability distribution over the possible samples from a defined population:For sampling distinct labelled units, $s$ is a subset of the population. The design includes the selection mechanism, not merely the final sample size. A procedure with repeated draws can instead require a distribution over ordered sequences; the resulting inclusion probabilities concern whether a label appears at least once.

The design determines inclusion probabilities and joint inclusion probabilities throughThese connect the physical sampling procedure to the Horvitz-Thompson estimator and its sampling variance.^[ref]

An original comparison uses four fixed values $(0,0,10,10)$ and selects two units. Under simple random sampling, every one of the six pairs has probability $1/6$. Each ${\pi }_i=1/2$, so the Horvitz-Thompson estimator is twice the selected sum. It takes values $0,20,40$ with probabilities $1/6,4/6,1/6$. Its expected value is the true finite population total $20$, and its variance isA second design chooses only $\{1,2\}$ or $\{3,4\}$, each with probability $1/2$. It has the same sample size and the same individual inclusion probabilities, but the estimator is now $0$ or $40$, giving variance $400$. Pair selection matters even when individual representation agrees.

A third design chooses uniformly among the four pairs containing one label from each of those two groups. Its total estimate is always $20$ for this particular population. This illustrates the potential gain from stratified sampling when groups correspond to relevant variation, not a claim that the third design dominates for every possible population. A reproducible design records the eligible labels and actual random selection rule before interpreting the observed sample.

The single-draw variance identifiability obstruction is the inability, for an unrestricted finite population, to construct a universally design-unbiased estimate of a total estimator's variance from just one observed unit. The estimator can estimate the total unbiasedly while the required cross-products for estimating its sampling variance are never jointly observed. This is an obstruction to the stated one-draw estimation problem, not a claim that a parameter can never be identified from repeated independent samples.

An original two-unit proof makes the distinction exact. Sample label $I=1$ or2 with equal probability and observe only $y_I$. The Horvitz-Thompson estimator of the finite population total is $\hat{T}=2y_I$. Its expectation is $y_1+y_2$, and its design variance isSuppose a proposed variance estimate returns $g_1(y_1)$ after observing unit1 and $g_2(y_2)$ after observing unit2. Unbiasedness for every pair would requireThe left side is additive in the two unknown values, while the right side contains an unobserved product.

To prove impossibility without smoothness assumptions, evaluate both sides at $(y_1,y_2)=(0,0),(1,0),(0,1),(1,1)$. Every additive function $H$ has rectangular contrast $H(1,1)-H(1,0)-H(0,1)+H(0,0)=0$. The required variance has contrast $0-1-1+0=-2$. The contradiction rules out such an unbiased estimator. Independent auxiliary randomization does not help: averaging it out merely replaces each $g_i$ by another function of the same single observed value.

Here the individual inclusion probabilities are positive, but the joint inclusion probability for distinct units is zero. The usual Horvitz-Thompson variance estimator needs pairwise inclusion positivity to turn sample cross-products into unbiased population cross-product estimates. The primary analysis of zero pairwise probabilities identifies precisely these missing terms.^[ref]

Extra information can change the problem: observing suitable pairs, imposing a justified population relation, or accepting a model-based or conservative estimate uses assumptions absent from the proof. Observing one value also does not prevent writing a biased variance estimate. What fails is a single-draw unbiased estimator valid for every unrestricted population, even though the total estimator itself remains unbiased.