How to Make Causal Inferences with Time-Series Cross-Sectional Data Under Selection on Observables

47 Pages Posted: 11 May 2018

See all articles by Matthew Blackwell

Matthew Blackwell

Department of Government, Harvard University

Adam Glynn

Harvard University

Date Written: May 2018

Abstract

Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, allow researchers to estimate a broad set of causal quantities, including contemporaneous and lagged treatment effects. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under very strong assumptions. In this paper, we use potential outcomes to define causal quantities of interest in this settings and clarify how standard models like the autoregressive distributed lag model can produce biased estimates of these quantities due to post-treatment conditioning. We then describe two estimation strategies that avoid these post-treatment biases—inverse probability weighting and structural nested mean models—and show via simulations that they can outperform standard approaches in small sample settings. We illustrate these methods in a study of how welfare spending affects terrorism.

Suggested Citation

Blackwell, Matthew and Glynn, Adam, How to Make Causal Inferences with Time-Series Cross-Sectional Data Under Selection on Observables (May 2018). V-Dem Working Paper 2018:67. Available at SSRN: https://ssrn.com/abstract=3176910 or http://dx.doi.org/10.2139/ssrn.3176910

Matthew Blackwell (Contact Author)

Department of Government, Harvard University ( email )

1737 Cambridge Street
Cambridge, MA 02138
United States

HOME PAGE: http://gov.harvard.edu

Adam Glynn

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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