Applying Causal Inference Methods in Psychiatric Epidemiology

Causal inference in environmental epidemiology

Inferring causality is necessary to achieve the goal of epidemiology, which is to elucidate the cause of disease. Causal inference is conducted in three steps: evaluation of validity of the study, inference of general causality, and inference of individual causality. To evaluate validity of the study, we propose a checklist that focuses on biases and generalizability. For general causal inferen...

Frameworks for Causal Inference in Epidemiology

[In St. Louis, in 1883, the population density of 9.8 persons to the acre] is indeed a low density compared with that of most metropolitan cities: that of London, for instance, is given at 52.5 to the acre in 1883. And yet we find the annual rate of mortality per thousand in London in 1883 to have been but 20.4, while that of St. Louis was 21.35. With such a variance existing in the relative de...

Causation and causal inference in epidemiology.

Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multi-causality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component cause...

Demystifying Causal Inference In Air Pollution Epidemiology

The regulatory environment surrounding air pollution control policies warrants a new type of epidemiological evidence. Whereas air pollution epidemiology has typically informed policies with estimates of exposureresponse relationships between pollution and health outcomes, these estimates alone cannot support current debates surrounding the actual health impacts of air quality regulations. This...

Matching Methods for Causal Inference