Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Tyler
VanderWeele*, Professor of Epidemiology at Harvard University. He will be
presenting work entitled *Sensitivity analysis in observational research:
introducing the E-value*. Please find the abstract below and on the
Applied Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.
As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided. See you all there!
-- Dana Higgins
*Title:* *Sensitivity analysis in observational research: introducing the
E-value*
*Abstract:* Sensitivity analysis is useful in assessing how robust an
association is to potential unmeasured or uncontrolled confounding. This
webinar introduces a new measure called the “E-value,” which is related to
the evidence for causality in observational studies that are potentially
subject to confounding. The E-value is defined as the minimum strength of
association, on the risk ratio scale, that an unmeasured confounder would
need to have with both the treatment and the outcome to fully explain away
a specific treatment–outcome association, conditional on the measured
covariates. A large E-value implies that considerable unmeasured
confounding would be needed to explain away an effect estimate. A small
E-value implies little unmeasured confounding would be needed to explain
away an effect estimate. The speaker and his collaborators propose that in
all observational studies intended to produce evidence for causality, the
E-value be reported or some other sensitivity analysis be used. They
suggest calculating the E-value for both the observed association estimate
(after adjustments for measured confounders) and the limit of the
confidence interval closest to the null. If this were to become standard
practice, the ability of the scientific community to assess evidence from
observational studies would improve considerably, and ultimately, science
would be strengthened.
Reference: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in
observational research: introducing the E-value. Annals of Internal
Medicine, 167:268-274.
Online E-value Calculator: https://mmathur.shinyapps.io/evalue/
Hi everyone!
Welcome back to Applied Statistics! This week at the Applied Statistics
Workshop we will be welcoming *Laura Nelsom*, Assistant Professor of
Sociology and Anthropology at Northeastern University. She will be
presenting work entitled* Computational Means, Qualitative Ends*. Please
find the abstract below and on the Applied Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.
As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided. See you all there!
-- Dana Higgins
*Title:* *Computational Means, Qualitative Ends*
*Abstract:* The majority of scholarship in the growing field of
computational social science is focused on quantitative projects aimed at
identifying generalizablefeatures of broad social systems. A smaller group
of scholars are instead weaving computational methods into qualitative
approaches to provide comprehensive, reproducible, but deeply contextual
descriptions of more narrow empirical phenomena. To explore this
intersection of computational and qualitative methods, we gathered over
430,000 newspaper articles describing the actions and beliefs of 530
environmental movement organizations between 1990 and 2015. Combining
qualitative and interpretive methods with computational techniques,
primarily natural language processing techniques and machine learning, we
provide a rich, meaningful, but computational description of this movement
sector andhow it has changed over time. We focus on three questions: 1)
what is the full range of tactical and strategic repertoires within the
environmental movement sector? 2) how have these repertoires changed over
time? and 3) can we inductively but computationally identify social
movement form via shared tactical and strategic repertoires? In exploring
these three questions we identified a fourth question: 4) what internal
movement processes led to the emergence of a new environmental form, a form
that focuses almost exclusively on business sustainability? Using various
clustering techniques, we explore this last question, the emergence of a
new environmental movement form, to think through the implications of using
computational methods for qualitative ends.