Hi all,
Our next virtual meeting will be at 12pm (EST) Wednesday, March 17, where
Suzanna Linn (Penn State University) presents research on "Causal
Inference in Dynamic Systems." This is joint work with Clay Webb.
*Abstract*:
A causal inference revolution has been under way in political methodology
for the better part of the last decade. Time series analysts have not been
major contributors to this revolution because the tools that have been
developed thus far do not fit our data. Existing methods either require
analyst to pool observations so that analysts can differentiate treatment
and control units, require analysts to identify or develop suitable control
series, or require the analyst to exercise control over treatment in a time
series experiment. Our goal is to identify the assumptions and conditions
required for analysts to make causal inferences with observational time
series data when observations cannot be pooled, control series are
unavailable, and counterfactuals cannot be forecast. We highlight the
critical assumptions for causal identification in structural dynamic
systems: partial equilibrium recursivity and conditional exogeneity. We
discuss the conditions when these assumptions are plausible, outline tests
for conditional exogeneity and structural non-causality, and consider the
potential limitations of the proposed framework. When the proposed
assumptions are met, standard Granger non-causality tests provide a means
for analysts to recover causal estimands.
*Zoom link*:
https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all on Wednesday!
Best,
Soichiro
--
Soichiro Yamauchi
PhD candidate
Harvard University
URL:
https://soichiroy.github.io/