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National Institute for Applied Statistics Research Australia Seminar Series

October 10 @ 1:30 pm - 2:30 pm


Rune Christiansen (krunechristiansen@math.ku.dk)

Department of Mathematical Sciences

University of Copenhagen



Using space as an anchor: causality meets spatial data



In statistical causality, we are interested not only in modeling the behaviour

of a system that is passively observed, but also how the system reacts

to changes in the data generating mechanism. Controlled executions of

such interventions (randomized trials) are viewed as the gold standard for

learning causal models, but are often practically impossible to conduct.

When controlled interventions are infeasible, it has been suggested

to exploit natural variations resulting from external forces on the system.

Such scenarios are common in spatial statistics: local conditions in which the

system of interest is imbedded are rarely constant across large spatial domains,

and strong heterogeneity is thus a typical characteristic of spatial data sets.

In this talk, I present ideas on how the two fields could benefit each other.

In particular, I discuss how spatial heterogeneity might be used to infer

causal relationships (via the instrumental variables approach), and introduce

a nonlinear version of anchor regression, which is an an estimation procedure

that encourages regression models to be (more) spatially invariant.

No prior knowledge of causality is required.



Building 1 Room G05
University of Wollongong
Wollongong, New South Wales 2500
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