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Department of Epidemiology

Causal Inference

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Causal Inference

Researchers in this area develop, refine, or apply epidemiological, statistical, and other approaches to understand how the world works.

Describing patterns of diseases, medications, or other phenomena is often not sufficient to improve human health. Understanding why particular patterns exist and the potential impact of intervening to change such patterns is also often necessary. The complex scientific tasks of obtaining such an understanding can be called “causal inference”. These tasks include specifying knowledge about a system that researchers wish to study in a causal model (e.g., a causal directed acyclic graph), identifying observed data (e.g., administrative health insurance claims, electronic health records), linking the observed data to the causal model, translating the research question into target quantities, and then working to assess identifiability of those quantities, estimate them properly, and carefully interpret the estimates.

Our researchers endeavor to develop, refine, or apply new approaches that enable the successful use of data to accurately estimate disease burden; understand the effects of treatments and other exposures; and improve human health and well-being in the real world. 

Faculty

  • Howe

    Chanelle Howe

    Associate Professor
  • Zullo

    Andrew Zullo

    Associate Professor of Epidemiology and of Health Services, Policy and Practice
    Andrew_Zullo@brown.edu
  • Nina Joyce

    Nina Joyce

    Assistant Professor
    nina_joyce@brown.edu
  • Gantenberg

    Jason Gantenberg

    Assistant Professor of the Practice
    jason_gantenberg@brown.edu
  • ackley

    Sarah Ackley

    Assistant Professor
    sarah_ackley@brown.edu
Brown University School of Public Health
Providence RI 02903 401-863-3375 public_health@brown.edu

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Causal Inference