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Annu Rev Public Health. 2019 Apr 1;40:7-21. doi: 10.1146/annurev-publhealth-040218-043735. Epub 2019 Jan 2.

Commentary: Causal Inference for Social Exposures.

Author information

1
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec H3A 1A2, Canada; email: jay.kaufman@mcgill.ca.

Abstract

Social epidemiology seeks to describe and quantify the causal effects of social institutions, interactions, and structures on human health. To accomplish this task, we define exposures as treatments and posit populations exposed or unexposed to these well-defined regimens. This inferential structure allows us to unambiguously estimate and interpret quantitative causal parameters and to investigate how these may be affected by biases such as confounding. This paradigm has been challenged recently by some critics who favor broadening the exposures that may be studied beyond treatments to also consider states. Defining the exposure protocol of an observational study is a continuum of specificity, and one may choose to loosen this definition, incurring the cost of causal parameters that become commensurately more vague. The advantages and disadvantages of broader versus narrower definitions of exposure are matters of continuing debate in social epidemiology as in other branches of epidemiology.

KEYWORDS:

causal inference; confounding; counterfactual; quasi-experimental; race/ethnicity; social determinants; well-defined exposure

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