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Alan M. Zaslavsky develops statistical methods for measuring health-care quality and disparities. His research includes missing data, hierarchical Bayesian models and Bayesian inference, bringing statistical adjustment to comparisons between health-care providers and populations.^[ref]

With Fan Li and Kari Lock Morgan, he developed a framework for balancing covariates through propensity score weighting. Their overlap weights emphasize people with a substantial chance of receiving either treatment, defining an overlap-weighted average treatment effect for that comparison population.^[ref]

An augmented inverse-probability-weighted estimator averages a regression prediction plus an inverse-probability-weighted residual. In the treatment setting of doubly robust estimation, let ${\hat{m}}_a(X_i)$ estimate the conditional outcome mean and ${\hat{e}}_a(X_i)$ the probability of receiving treatment $a$. ThenThe first term predicts outcomes for everyone; the weighted residual corrects prediction errors using observed outcomes from the corresponding treatment group. The conditional-expectation calculation in doubly robust estimation makes the remaining bias a product of outcome-regression and propensity-score errors. An exactly correct outcome regression makes the residual correction mean zero even with an incorrect propensity score; a correct propensity score makes that correction repair the regression bias.

The nuisance estimates are commonly obtained by cross-fitting, with each observation evaluated using functions trained on other observations. Both treatment probabilities must remain positive on the population being studied, and small fitted probabilities can make the variance very large. Double robustness concerns consistency under one correct nuisance model; it does not automatically deliver valid finite-sample or asymptotic confidence intervals.

Caliper matching permits a candidate matching pair only when a prespecified distance is at most a tolerance $c$. For propensity score $e(X)$, possible rules are $|e(X_i)-e(X_j)|\le c$ or $|\mathrm{logit}e(X_i)-\mathrm{logit}e(X_j)|\le c$. These are different distances; the numeric tolerance must be interpreted on the selected scale.^[ref]

For a treated unit with propensity score $0.40$, a raw-score caliper $c=0.03$ admits a control with score $0.42$ and excludes one with score $0.46$. The matching algorithm still has to choose among admissible controls and decide whether controls may be reused. A tighter caliper excludes distant pairs but can leave more treated units unmatched. Excluding those units changes the population represented by the matched estimate; a caliper is not a guarantee of an unchanged average treatment effect on the treated. Check covariate balance after matching, and account for the paired or reused-control structure in statistical inference.

Fan Li is the Duke Statistical Science researcher who coauthored Balancing Covariates via Propensity Score Weighting by Fan Li, Kari Lock Morgan and Alan M. Zaslavsky. Her author homepage and the paper's institutional byline identify this particular researcher.^[ref]

For propensity score $e(X)$, overlap weights assign weight $1-e(X)$ to a treated person and $e(X)$ to an untreated person. These bounded weights emphasize shared treatment eligibility and target a population with density proportional to $e(X)(1-e(X))$. Causal interpretation still requires the relevant no unmeasured confounding assumption and positivity in causal inference.^[ref]

For binary treatment, outcomes $Y_i$, and estimated propensity score $\hat{e}(X_i)$, the inverse-probability-weighted estimator isIt is consistent under conditional exchangeability, consistency of potential outcomes, positivity in causal inference, and a consistent propensity-score estimator.

Inverse probability weighting weights observations by reciprocals of the probabilities that generated their observed assignment or selection. For binary treatment with propensity score $e(X)$, the average-treatment-effect observation weight isIt uses the probability of the treatment actually received. Under conditional exchangeability, consistency in causal inference and positivity, the identification identity isTo prove it, condition on $X$: the assignment probability cancels its denominator, leaving $\mathbb{E}(Y\mid A=a,X)$. Average over $X$ and apply the causal identification assumptions. The same cancellation with a function $g(X)$ instead of $Y$ proves population balance of the measured covariates under correctly specified assignment probabilities.

If a stratum has treatment probability $0.2$, each treated observation there receives weight five: it represents units with the same measured baseline characteristics who usually receive the other treatment. Very small assignment probabilities give large weights and unstable estimates. Estimated assignment models, trimming and normalization can change finite-sample behavior and, for trimming, the target population. Overlap weights deliberately define a different target rather than estimating the same full-population contrast by another name.

Kari Lock Morgan studies the design of randomized experiments, especially how to improve baseline covariate balance. Her work on restricted randomization specifies acceptable balance before an experiment and redraws allocations that fail that criterion. Inference must account for the resulting assignment procedure.^[ref]

With Fan Li and Alan M. Zaslavsky, she studied propensity score weighting for observational studies. For a logistic propensity score fitted by maximum likelihood estimation with an intercept, their overlap weights exactly balance weighted means of the included covariates under the stated fitting conditions.^[ref]

Matching constructs comparisons between units receiving different treatments but having similar pretreatment covariates. Similarity can be measured directly on selected covariates or through a propensity score. The design specifies the distance, allowed discrepancies, number of controls per treated unit and whether a control can be reused.

With one distinct control matched to each of $n_T$ treated units, a simple estimator isThis averages differences in the represented treated population. Dropping unmatched treated units changes that population, and reusing controls induces dependence between pair differences. Caliper matching explicitly limits allowed discrepancies.

Matching seeks covariate balance; it does not create a randomized controlled trial. Identifying a causal effect still requires conditional exchangeability given the matched pretreatment covariates, adequate positivity, and consistency in causal inference. Unmeasured determinants of assignment and outcome can remain different even when measured covariates are well balanced. Conditioning on a causal mediator or inducing collider bias is not repaired by calling the procedure matching.

A nuisance function is an unknown function needed to identify or estimate a target statistical parameter but not itself the primary estimand. In treatment-effect estimation, examples are the propensity score $e(x)=\mathbb{P}(A=1\mid X=x)$ and the outcome-regression functions $m_a(x)=\mathbb{E}[Y\mid A=a,X=x]$.

These functions can be infinite-dimensional even when the target is one number. Estimating them poorly can bias that target, so nuisance does not mean irrelevant. Doubly robust estimation and Neyman orthogonality control certain nuisance errors, while cross-fitting separates training observations from score evaluation. None of these devices removes the identification assumptions needed to interpret an observational treatment comparison causally.^[ref]

For binary treatment and propensity score $e(X)=P(A=1\mid X)$, the observed-unit overlap weights areEach unit receives the probability of assignment to the opposite group. These weights are bounded between zero and one.^[ref]

The common target-population tilting function is instead $h(X)=e(X)\{1-e(X)\}$. If the original covariate density is $f(X)$, then the unnormalized weighted densities in the two arms are equal:Thus $h$ describes the target population, while $w_1,w_0$ are the arm-specific observation weights. Multiplying every observed unit by $h(X)$ would not give this balance identity.

Under conditional exchangeability, consistency in causal inference and nonzero total overlap, the weighted contrast identifies the overlap-weighted average treatment effectUnits with deterministic treatment assignment have $h=0$ and contribute nothing to this target. The estimand therefore generally differs from the full-population average treatment effect.

The overlap-weighted average treatment effect averages the conditional average treatment effect with weight proportional to $e(X)\{1-e(X)\}$, where $e(X)$ is the propensity score. It emphasizes covariate strata in which both treatments are plausible.

Let $e(x)=\mathbb{P}(A=1\mid X=x)$ and let $\hat{e}$ be a consistent estimate. The inverse-probability-weighted estimator of the average treatment effect isUnder the exchangeability, consistency, and positivity conditions in part ii, and consistent estimation of the propensity score, its probability limit isso it consistently estimates the average treatment effect. It does not require the additive outcome-regression model used to interpret the ordinary-least-squares coefficient.

Estimate the propensity score $\pi$ and the untreated outcome regression $\mu$, preferably with cross-fitting when flexible methods are used, and setWith $n_1={\sum }_i{A}_i$, the resulting one-step estimator of the ATT isEquivalently,This is an augmented inverse-probability-weighted estimator; it is consistent when either the propensity model or the untreated outcome model is correct, subject to the usual regularity and positivity conditions.

Paul R. Rosenbaum develops the design and analysis of observational studies. His work asks how a study can support a causal inference when investigators cannot assign the exposure by randomization. His research includes matching, sensitivity analysis for unmeasured confounding and ways to design studies whose conclusions withstand larger hidden biases.^[ref]

With Donald B. Rubin, he established the central role of the propensity score $e(X)=P(A=1\mid X)$: conditioning on this score balances measured baseline covariates between treatment groups. Under the required treatment-assignment assumptions and positivity in causal inference, the score permits adjustment for a many-dimensional $X$ through a scalar. Measured balance alone leaves the possibility of unmeasured confounding.^[ref]

Peter C. Austin develops methods for observational studies, including propensity score matching. In his simulation study of caliper matching, treated and untreated people are paired only when their scores are sufficiently close.^[ref]

The caliper creates a practical tradeoff: a wider tolerance can retain more pairs but allow larger differences in measured baseline risk; a tighter tolerance can discard people and change the population represented by the comparison. Austin compared resulting bias, mean squared error and confidence interval coverage under specified data-generating scenarios. His numerical recommendations concern those estimands and scenarios, rather than a guarantee that matching removes unmeasured confounding.^[ref]

The propensity score for binary treatment $A$ and pretreatment covariates $X$ is $e(X)=P(A=1\mid X)$. It is a balancing score:This distributional fact does not require conditional exchangeability of the potential outcomes. It follows from the definition of the true assignment probability.^[ref]

For a bounded function $g$, condition successively on $X$ and on $e=e(X)$:Also $P(A=1\mid e)=e$, so division by $e$ gives the same conditional covariate distribution among treated units as in the full score stratum; the analogous argument uses $1-e$ for controls. Comparison of both arms requires $0<e<1$.

Identifying a causal effect is an additional step. If $(Y(0),Y(1))\perp A\mid X$, then the balancing property lets the scalar propensity score replace the full measured $X$ in that exchangeability statement. Without this causal assumption, balancing measured covariates leaves possible unmeasured confounding. An estimated score also need not deliver exact finite-sample covariate balance; its estimation and the subsequent matching in causal inference or weighting procedure must be checked.