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- W4387687128 abstract "Observational studies often use linear regression to assess the effect of ambient air pollution on outcomes of interest, such as human health or crop yields. Yet pollution datasets are typically noisy and include only a subset of potentially relevant pollutants, giving rise to both measurement error bias (MEB) and omitted variable bias (OVB). While it is well understood that these biases exist, less is understood about whether these biases tend to be positive or negative, even though it is sometimes falsely claimed that measurement error simply biases regression coefficient estimates towards zero. In this paper, we show that more can be said about the direction of these biases under the realistic assumptions that the concentrations of different types of air pollutants are positively correlated with each other and that each type of pollutant has a nonpositive association with the outcome variable. In particular, we demonstrate both theoretically and using simulations that under these two assumptions, the OVB will typically be negative and that more often than not the MEB for null pollutants or pollutants that are perfectly measured will be negative. We also provide precise conditions, which are consistent with the assumptions, under which we prove that the biases are guaranteed to be negative. While the discussion in this paper is motivated by studies assessing the effect of air pollutants on crop yields, the findings are also relevant to regression-based studies assessing the effect of air pollutants on human health outcomes." @default.
- W4387687128 created "2023-10-17" @default.
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- W4387687128 date "2023-10-12" @default.
- W4387687128 modified "2023-10-18" @default.
- W4387687128 title "Biases in estimates of air pollution impacts: the role of omitted variables and measurement errors" @default.
- W4387687128 doi "https://doi.org/10.48550/arxiv.2310.08831" @default.
- W4387687128 hasPublicationYear "2023" @default.
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