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      Best Practices for Gauging Evidence of Causality in Air Pollution Epidemiology

      1 , 1

      American Journal of Epidemiology

      Oxford University Press (OUP)

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          Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

          Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. 2004 John Wiley & Sons, Ltd.
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            Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy.

            This paper's findings suggest that an arbitrary Chinese policy that greatly increases total suspended particulates (TSPs) air pollution is causing the 500 million residents of Northern China to lose more than 2.5 billion life years of life expectancy. The quasi-experimental empirical approach is based on China's Huai River policy, which provided free winter heating via the provision of coal for boilers in cities north of the Huai River but denied heat to the south. Using a regression discontinuity design based on distance from the Huai River, we find that ambient concentrations of TSPs are about 184 μg/m(3) [95% confidence interval (CI): 61, 307] or 55% higher in the north. Further, the results indicate that life expectancies are about 5.5 y (95% CI: 0.8, 10.2) lower in the north owing to an increased incidence of cardiorespiratory mortality. More generally, the analysis suggests that long-term exposure to an additional 100 μg/m(3) of TSPs is associated with a reduction in life expectancy at birth of about 3.0 y (95% CI: 0.4, 5.6).
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              Toward Causal Inference With Interference.

              A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Relations among the estimands are established; for example, the total causal effect is shown to equal the sum of direct and indirect causal effects. Using an experimental design with a two-stage randomization procedure (first at the group level, then at the individual level within groups), unbiased estimators of the proposed estimands are presented. Variances of the estimators are also developed. The methodology is illustrated in two different settings where interference is likely: assessing causal effects of housing vouchers and of vaccines.
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                Author and article information

                Journal
                American Journal of Epidemiology
                Oxford University Press (OUP)
                0002-9262
                1476-6256
                December 15 2017
                December 15 2017
                September 06 2017
                December 15 2017
                December 15 2017
                September 06 2017
                : 186
                : 12
                : 1303-1309
                Affiliations
                [1 ]Department of Biostatistics, Harvard H.T. Chan School of Public Health, Boston, Massachusetts
                Article
                10.1093/aje/kwx307
                © 2017

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