40 research outputs found
Replication Data for: Causal Inference for Interfering Units With Cluster and Population Level Treatment Allocation Programs
No full textThis data set consists the analysis data set for the paper titled "Causal Inference for Interfering Units With Cluster and Population Level Treatment Allocation Programs". It includes key power plant covariates, area level characteristics and ambient ozone concentrations with 100 km of the power plant
Replication Data for: Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching
No full textThis data set consists the analysis data set for the paper titled "Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching". It includes key power plant covariates and area level characteristics. Power plants are linked to ozone and temperature information
Replication Data for: Causal Inference for Interfering Units With Cluster and Population Level Treatment Allocation Programs
No full textThis data set consists the analysis data set for the paper titled "Causal Inference for Interfering Units With Cluster and Population Level Treatment Allocation Programs". It includes key power plant covariates, area level characteristics and ambient ozone concentrations with 100 km of the power plant
Replication Data for: Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching
No full textThis data set consists the analysis data set for the paper titled "Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching". It includes key power plant covariates and area level characteristics. Power plants are linked to ozone and temperature information
sj-docx-1-ctj-10.1177_17407745211056875 – Supplemental material for Clarifying selection bias in cluster randomized trials
No full textSupplemental material, sj-docx-1-ctj-10.1177_17407745211056875 for Clarifying selection bias in cluster randomized trials by Fan Li, Zizhong Tian, Jennifer Bobb, Georgia Papadogeorgou and Fan Li in Clinical Trials</p
sj-docx-2-ctj-10.1177_17407745211056875 – Supplemental material for Clarifying selection bias in cluster randomized trials
No full textSupplemental material, sj-docx-2-ctj-10.1177_17407745211056875 for Clarifying selection bias in cluster randomized trials by Fan Li, Zizhong Tian, Jennifer Bobb, Georgia Papadogeorgou and Fan Li in Clinical Trials</p
Ozone, Temperature and Census Raw Data
No full textThis is the raw data file for ozone, and temperature derived from the EPA Air Quality System, and data from Census 2000
Ozone, Temperature and Census Raw Data
No full textThis is the raw data file for ozone, and temperature derived from the EPA Air Quality System, and data from Census 2000
Power Plant Emissions Data
No full textThis data set contains data on emissions (and other characteristics) at the level of Electricity Generating Unit (EGU). These data were derived from the EPA Air Markets Program Data (AMPD)
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Causal Inference Methods in Air Pollution Research
No full textWhile the air pollution concentrations in the United States continue to decrease, one important and politically charged question remains: Is long-term exposure to low levels of air pollution still harmful? Several approaches have been developed to estimate the relationship between exposure to particulate matter with diameter at most 2.5 micrometers (PM2.5) and various health outcomes. However, none of these approaches account for the fact that different variables might act as confounders of the exposure response relationship at different exposure levels.
In chapter 1, we developed a Bayesian methodology for the estimation of the causal exposure-response curve for exposure to PM2.5 on cardiovascular hospitalizations. This method allows for flexible estimation of the shape of the exposure-response relationship, and for differential confounding adjustment at different levels of the exposure. Moreover, it provides a principled way to identify the confounding importance of different predictors at different exposure levels.
Over the last few decades, there have been various regulations in the Unites States aiming to reduce emissions from power plants with the ultimate goal of reducing ambient air pollution concentrations and pollution-related hospitalizations. However, the effectiveness of these regulations has not been adequately studied. Since nitric oxide and nitrogen dioxides (NOx) are important precursors of ozone formation, we focused on the comparative effectiveness of a class of NOx emission reduction technologies against alternatives on ambient ozone concentrations.
In chapter 2, we developed causal inference methodology rooted in propensity score matching to adjust for unobserved spatial confounding, such as unmeasured weather and atmospheric conditions. We showed that unobserved confounding by spatial variables is likely to be present, and that incorporating spatial proximity in the matching of treated units to control units returns effect estimates that are more in line with subject-matter knowledge.
In chapter 3, we addressed the issue of interference in the studies of air pollution regulations. The movement of emissions and air pollution leads to interference, since interventions that take place at one power plant can affect air pollution levels in the area surrounding other power plants. In more detail, assuming that the power plants can be clustered in groups within which there is interference but not across them, we defined new estimands for causal inference with interfering units that correspond to quantities of interest under realistic treatment allocation programs. Consistent estimators and asymptotic results were derived and were employed to quantify the comparative effectiveness of NOx emission control technologies.Biostatistic
