Suzanna Linn (Workshop in Applied Statistics)


Wednesday, March 17, 2021, 12:00pm to 1:30pm


Zoom - see below

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Today's presenter:

Suzanna Linn (Penn State University), "Causal Inference in Dynamic Systems"

Conference link:


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.

The Applied Statistics Workshop (Gov 3009) is a forum for graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. It is co-hosted by Harvard University's Department of Government and Institute for Quantitative Social Science (IQSS).

For more information, visit the Applied Statistics website.