arXiv subjects

Mara A. McAdams-DeMarco

Publications and source records attributed to Mara A. McAdams-DeMarco.

3 recordsLinked to original sources

arXiv2025

Comparing causal parameters with many treatments and positivity violations

Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each targeting a different treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption, that every subject has a non-zero probability of receiving each treatment. This is often implausible, especially when treatment can take many values. Causal parameters based on dynamic stochastic interventions offer robustness to positivity violations. However, comparing these parameters may fail to reflect the effects of the underlying target treatments because the parameters can depend on outcomes under non-target treatments. To clarify when two parameters targeting different treatments yield a useful comparison of treatment efficacy, we propose a comparability criterion: if the conditional treatment-specific mean for one treatment is greater than that for another, then the corresponding causal parameter should also be greater. Many standard parameters fail to satisfy this criterion, but we show that only a mild positivity assumption is needed to identify parameters that yield useful comparisons. We then provide two simple examples that satisfy this criterion and are identifiable under the milder positivity assumption: trimmed and smooth trimmed treatment-specific means with multi-valued treatments. For smooth trimmed treatment-specific means, we develop doubly robust-style estimators that attain parametric convergence rates under nonparametric conditions. We illustrate our methods with an analysis of dialysis providers in New York State.

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arXiv2025

Asymptotically Efficient Data-adaptive Penalized Shrinkage Estimation with Application to Causal Inference

A rich literature exists on constructing non-parametric estimators with optimal asymptotic properties. In addition to asymptotic guarantees, it is often of interest to design estimators with desirable finite-sample properties; such as reduced mean-squared error of a large set of parameters. We provide examples drawn from causal inference where this may be the case, such as estimating a large number of group-specific treatment effects. We show how finite-sample properties of non-parametric estimators, particularly their variance, can be improved by careful application of penalization. Given a target parameter of interest we derive a novel penalized parameter defined as the solution to an optimization problem that balances fidelity to the original parameter against a penalty term. By deriving the non-parametric efficiency bound for the penalized parameter, we are able to propose simple data-adaptive choices for the L1 and L2 tuning parameters designed to minimize finite-sample mean-squared error while preserving optimal asymptotic properties. The L1 and L2 penalization amounts to an adjustment that can be performed as a post-processing step applied to any asymptotically normal and efficient estimator. We show in extensive simulations that this adjustment yields estimators with lower MSE than the unpenalized estimators. Finally, we apply our approach to estimate provider quality measures of kidney dialysis providers within a causal inference framework.

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arXiv2024

Doubly Robust Nonparametric Efficient Estimation for Provider Evaluation

Provider profiling has the goal of identifying healthcare providers with exceptional patient outcomes. When evaluating providers, adjustment is necessary to control for differences in case-mix between different providers. Direct and indirect standardization are two popular risk adjustment methods. In causal terms, direct standardization examines a counterfactual in which the entire target population is treated by one provider. Indirect standardization, commonly expressed as a standardized outcome ratio, examines the counterfactual in which the population treated by a provider had instead been randomly assigned to another provider. Our first contribution is to present nonparametric efficiency bound for direct and indirectly standardized provider metrics by deriving their efficient influence functions. Our second contribution is to propose fully nonparametric estimators based on targeted minimum loss-based estimation that achieve the efficiency bounds. The finite-sample performance of the estimator is investigated through simulation studies. We apply our methods to evaluate dialysis facilities in New York State in terms of unplanned readmission rates using a large Medicare claims dataset. A software implementation of our methods is available in the R package TargetedRisk.

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