[Amsterdam Causality Meeting] Talk by Thomas Richardson Thursday Feb 6 at 10:00 in Science Park 107 room F3.20

[Joris Mooij]

Hi all,

We have another upcoming talk that may be of interest to you.
(Feel free to consider it a Amsterdam Causality Meeting "light" event with
just a single talk and no borrel afterwards!)

I'm happy to announce that Thomas Richardson will give a presentation on
Thursday February 6th at 10:00-11:00. You are cordially invited to join.

Date: Thursday February 6th, 10:00-11:00
Location: Science Park 107, room F3.20 (KdVI building, third floor)
Speaker: Thomas Richardson (University of Washington)
Title: Statistical analysis for the discrete instrumental variable model

Abstract: We consider causal instrumental variable (IV) models containing an
instrument (Z), a treatment (X) and a response (Y) in the case where X and Y
are binary, while Z is categorical taking k levels. We assume that the
instrument Z is randomized and has no direct effect on the outcome Y, except
through X.

In the first part of the talk we consider the problem of characterizing those
distributions over potential outcomes for Y that are compatible with a given
observed distribution P(X,Y | Z). We show that this analysis of identification
may be simplified by viewing the observed distribution as arising from a series
of observational studies on the same population. We also show that this
approach naturally leads to the restrictions imposed on the observed
distribution by the IV model.

In the second part of the talk we consider statistical inference for this
model. We first show that our characterization of the model for the observables
leads to a 'transparent’ approach to Bayesian inference under which identified
and non-identified parameters are clearly distinguished. We contrast this with
the ‘direct' approach that puts priors directly on the distribution of
potential outcomes.

Finally, time permitting, we will describe a frequentist approach to inference
for the IV model via a new approach to constructing confidence regions for
multinomial data with (non-asymptotic) coverage guarantees via a Chernoff-type
tail bound.

[Joint work with Robin J. Evans (Oxford), F. Richard Guo (University of
Michigan), James M. Robins (Harvard), Yilin Song (University of Washington) and
Gary Chan (University of Washington)


Best wishes,
Joris