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Activity Number: 164 - SPEED: Causal Inference and Related Methodology
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #329184 Presentation
Title: Embedding Observational Studies into Hypothetical Fractional-Factorial Experiments
Author(s): Nicole Pashley* and Marie-Abele Bind
Companies: Harvard University and Harvard University
Keywords: Causal inference; Observational data; Factorial design; Multiple treatments
Abstract:

Consider an analysis that aims to quantify the causal effects of the exposure to multiple pesticides on a health outcome. For ethical reasons, this important question cannot be answered by randomly assigning participants to different levels of pesticides. However, a precise and rigorous analysis of observational data can help answering this causal question. The reconstruction of hypothetical factorial randomized experiments is complex due to the lack of randomization in observational studies. Certain treatment combinations may also be so rare that, for some combinations, we have no measured outcomes. We propose to recreate a hypothetical fractional factorial experiment instead of a full-factorial experiment, and define the causal estimands of interest. We illustrate our method using biomedical data from NHANES and estimate the effects of four common pesticides on body mass index and C-reactive protein in a matched dataset. Finally, we contrast our method to standard model-based approaches in the original dataset.


Authors who are presenting talks have a * after their name.

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