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Abstract Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered data, there may be unmeasured cluster-level covariates that are related to both the treatment assignment and outcome. When such unmeasured cluster-specific confounders exist and are omitted in the propensity score model, the subsequent propensity score adjustment may be biased. In this article, we propose a calibration technique for propensity score estimation under the latent ignorable treatment assignment mechanism, i. e., the treatment-outcome relationship is unconfounded given the observed covariates and the latent cluster-specific confounders. We impose novel balance constraints which imply exact balance of the observed confounders and the unobserved cluster-level confounders between the treatment groups. We show that the proposed calibrated propensity score weighting estimator is doubly robust in that it is consistent for the average treatment effect if either the propensity score model is correctly specified or the outcome follows a linear mixed effects model. Moreover, the proposed weighting method can be combined with sampling weights for an integrated solution to handle confounding and sampling designs for causal inference with clustered survey data. In simulation studies, we show that the proposed estimator is superior to other competitors. We estimate the effect of School Body Mass Index Screening on prevalence of overweight and obesity for elementary schools in Pennsylvania.
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Causal inference with confounders missing not at random
Summary It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects.
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- PAR ID:
- 10142598
- Date Published:
- Journal Name:
- Biometrika
- Volume:
- 106
- Issue:
- 4
- ISSN:
- 0006-3444
- Page Range / eLocation ID:
- 875 to 888
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The odds ratio is a common measure to assess the association between abinary predictor variable and a binary outcome. In epidemiology, the outcome is often the dis- ease status, and the predictor of interest is a suspected risk factor for the disease. The purpose of the study is an attempt to establish a causal association between exposure and disease. If the object of a study is the estimation of a marginal odds ratio, defined as the ratio of the odds that would be observed in a population if everyone were exposed versus the odds in the same population if no one were exposed, methods such as the Mantel–Haenszel estimator are commonly used. When it is necessary to adjust for many confounders and/or continuous confounders, this approach results in a biased and incon- sistent estimator, including matching and stratification by the propensity score. An alter- native to matching is inverse probability weighting by the propensity score. The resulting estimator is consistent, provided the propensity score model is correct and adjusts for all confounders.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/biomet/asz048
