[gov3009-l] Applied Statistics - 10/28 - Judith Lok

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Oct 26 09:37:39 EDT 2015

Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Judith
Lok*, Associate Professor of Biostatistics at Harvard University's TH Chan
School of Public Health. She will be presenting work entitled *Defining and
estimating causal direct and indirect effects when setting the mediator to
specific values is not feasible.*  Please find the abstract below and on
the website

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website

-- Aaron Kaufman

Title: Defining and estimating causal direct and indirect effects when
setting the mediator to specific values is not feasible

Natural direct and indirect effects decompose the effect of a treatment
into the part that is mediated by a covariate (the mediator) and the part
that is not. Their definitions rely on the concept of outcomes under
treatment with the mediator ``set'' to its value without treatment.
Typically, the mechanism through which the mediator is set to this value is
left unspecified, and in many applications it may be challenging to fix the
mediator to particular values for each unit or individual. Moreover, how
one sets the mediator may affect the distribution of the outcome. This
presentation introduces ``organic'' direct and indirect effects, which can
be defined and estimated without relying on setting the mediator to
specific values. Organic direct and indirect effects can be applied for
example to estimate how much of the effect of some treatments for HIV/AIDS
on mother-to-child transmission of HIV-infection is mediated by the effect
of the treatment on the HIV viral load in the blood of the mother.

Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
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