[gov3009-l] Applied Stats - 3/11 - James Robins
astrezhnev at fas.harvard.edu
Mon Mar 9 10:31:28 EDT 2015
This week at the Applied Statistics Workshop we will be welcoming *James
Robins*, Mitchell L. and Robin LaFoley Dong Professor of Epidemiology in
the Departments of Epidemiology and Biostatistics at Harvard University. He
will be presenting work entitled *The Foundations of Statistics and Its
Implications for Current Methods for Causal Inference from Observational
and Randomized Trial Data*. Please find the abstract below and on the
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
Title: The Foundations of Statistics and Its Implications for Current
Methods for Causal Inference from Observational and Randomized Trial Data
Abstract: The foundations of statistics are the fundamental conceptual
principles that underlie statistical methodology and distinguish statistics
from the highly related fields of probability and mathematics. Examples of
foundational concepts include ancillarity, the conditionality principle,
the likelihood principle, statistical decision theory, the weak and strong
repeated sampling principle, coherence and even the meaning of probability
itself. In the 1950s and 1960s, the study of the foundations of statistics
held an important place in the field. However its central role faded with
the revolution in computing that offered the ability to actually do more
than just philosophize about how to analyze complex high dimensional data.
I discuss how these principles both inform and are informed by modern
approaches to causal analysis. Among other examples, I discuss from a
foundational perspective are (i) methods for model and/or covariate
selection including the issue of whether detailed balance on covariates is
needed after one stratifies on the true or estimated propensity, (ii) the
conflict between the minimization of MSE versus accuracy of confidence
intervals as inferential goals and (iii) the question of a whether
principled Baysesian inference must ignore the propensity score even when
it is known.
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