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

Published online by Cambridge University Press:  03 August 2018

MATTHEW BLACKWELL  and
ADAM N. GLYNN
MATTHEW BLACKWELL*
Affiliation:
Harvard University
ADAM N. GLYNN*
Affiliation:
Emory University
*
Matthew Blackwell is an Associate Professor, Department of Government and Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St., MA 02138. Web: http://www.mattblackwell.org ([email protected]).
Adam N. Glynn is an Associate Professor, Department of Political Science, Emory University, 327 Tarbutton Hall, 1555 Dickey Drive, Atlanta, GA 30322 ([email protected]).
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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 effects and direct effects of lagged treatments. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under some strong assumptions. In this paper, we use potential outcomes to define causal quantities of interest in these 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.

Type
Research Article
Information
American Political Science Review , Volume 112 , Issue 4 , November 2018 , pp. 1067 - 1082
Copyright
Copyright © American Political Science Association 2018 

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Footnotes

We are grateful to Neal Beck, Jake Bowers, Patrick Brandt, Simo Goshev, and Cyrus Samii for helpful advice and feedback and Elisha Cohen for research support. Any remaining errors are our own. This research project was supported by Riksbankens Jubileumsfond, Grant M13-0559:1, PI: Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden and by European Research Council, Grant 724191, PI: Staffan I. Lindberg, V-Dem Institute, University of Gothenburg, Sweden. Replication files are available on the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/SFBX6Z.

References

REFERENCES

Abbring, Jaap H., and van den Berg, Gerard J.. 2003. “The Nonparametric Identification of Treatment Effects in Duration Models.” Econometrica 71 (5): 1491–517.Google Scholar
Acharya, Avidit, Blackwell, Matthew, and Sen, Maya. 2016. “Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects.” American Political Science Review 110 (3): 512–29.Google Scholar
Beck, Nathaniel, and Jackman, Simon. 1998. “Beyond Linearity by Default: Generalized Additive Models.” American Journal of Political Science 42 (2): 596–627.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N.. 2011. “Modeling Dynamics in Time-Series–Cross-Section Political Economy Data.” Annual Review of Political Science 14 (1): 331–52.Google Scholar
Blackwell, Matthew. 2013. “A Framework for Dynamic Causal Inference in Political Science.” American Journal of Political Science 57 (2): 504–20.Google Scholar
Blackwell, Matthew. 2014. “A Selection Bias Approach to Sensitivity Analysis for Causal Effects.” Political Analysis 22 (2): 169–82.Google Scholar
Blackwell, Matthew, and Glynn, Adam. 2018. Replication data for “How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables.” doi:10.7910/DVN/SFBX6Z, Harvard Dataverse, V1. https://doi.org/10.7910/DVN/SFBX6Z.Google Scholar
Box, George E. P., Jenkins, Gwilym M., and Reinsel, Gregory C.. 2013. Time Series Analysis: Forecasting and Control. Hoboken, NJ: Wiley.Google Scholar
Burgoon, Brian. 2006. “On Welfare and Terror Social Welfare Policies and Political-Economic Roots of Terrorism.” Journal of Conflict Resolution 50 (2): 176–203.Google Scholar
Chernozhukov, Victor, Fernndez-Val, Ivn, Hahn, Jinyong, and Newey, Whitney. 2013. “Average and Quantile Effects in Nonseparable Panel Models.” Econometrica 81 (2): 535–80.Google Scholar
Cole, Stephen R., and Hernán, Miguel A.. 2008. “Constructing Inverse Probability Weights for Marginal Structural Models.” American Journal of Epidemiology 168 (6): 656–64.Google Scholar
De Boef, Suzanna, and Keele, Luke. 2008. “Taking Time Seriously.” American Journal of Political Science 52 (1): 185–200.Google Scholar
Gerber, Alan S., Gimpel, James G., Green, Donald P., and Shaw, Daron R.. 2011. “How Large and Long-lasting Are the Persuasive Effects of Televised Campaign Ads? Results from a Randomized Field Experiment.” American Political Science Review 105 (01): 135–50.Google Scholar
Goetgeluk, Sylvie, Vansteelandt, Sijn, and Goetghebeur, Els. 2008. “Estimation of Controlled Direct Effects.” Journal of the Royal Statistical Society, Series B (Statistical Methodology) 70 (5): 1049–66.Google Scholar
Hainmueller, Jens, and Hazlett, Chad. 2014. “Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach.” Political Analysis 22 (2): 143–68.Google Scholar
Hernán, Miguel A., Brumback, Babette A., and Robins, James M.. 2001. “Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments.” Journal of the American Statistical Association 96 (454): 440–8.Google Scholar
Imai, Kosuke, and Kim, In Song. 2017. “When Should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?” Working paper. Accessed June 27, 2018. http://imai.princeton.edu/research/files/FEmatch.pdf.Google Scholar
Imai, Kosuke, and Ratkovic, Marc. 2015. “Robust Estimation of Inverse Probability Weights for Marginal Structural Models.” Journal of the American Statistical Association 110 (511): 1013– 23.Google Scholar
McCaffrey, Daniel F., Ridgeway, Greg, and Morral, Andrew R.. 2004. “Propensity Score Estimation with Boosted Regression for Evaluating Causal Effects in Observational Studies.” Psychological Methods 9 (4): 403–25.Google Scholar
Ravikumar, Pradeep, Lafferty, John, Liu, Han, and Wasserman, Larry. 2009. “Sparse Additive Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71 (5): 1009–30.Google Scholar
Robins, James M. 1986. “A New Approach to Causal Inference in Mortality Studies with Sustained Exposure Periods-Application to Control of the Healthy Worker Survivor Effect.” Mathematical Modelling 7 (9-12): 1393–512.Google Scholar
Robins, James M. 1994. “Correcting for Non-Compliance in Randomized Trials Using Structural Nested Mean Models.” Communications in Statistics 23 (8): 2379–412.Google Scholar
Robins, James M. 1997. “Causal Inference from Complex Longitudinal Data.” In Latent Variable Modeling and Applications to Causality, Vol. 120. Lecture Notes in Statistics, edited by Berkane, M.. New York: Springer-Verlag, 69–117.Google Scholar
Robins, James M., Greenland, Sander, and Hu, Fu-Chang. 1999. “Estimation of the Causal Effect of a Time-Varying Exposure on the Marginal Mean of a Repeated Binary Outcome.” Journal of the American Statistical Association 94 (447): 687–700.Google Scholar
Robins, James M., Hernán, Miguel A., and Brumback, Babette A.. 2000. “Marginal Structural Models and Causal Inference in Epidemiology.” Epidemiology 11 (5): 550–60.Google Scholar
Rosenbaum, Paul R. 1984. “The Consequences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment.” Journal of the Royal Statistical Society, Series A (General) 147 (5): 656–66.Google Scholar
Rubin, Donald B. 1978. “Bayesian Inference for Causal Effects: The Role of Randomization.” Annals of Statistics 6 (1): 34– 58.Google Scholar
Shephard, Neil, and Bojinov, Iavor. 2017. “Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading.” Working paper. Accessed June 27, 2018. https://scholar.harvard.edu/files/shephard/files/cause20170718.pdf.Google Scholar
Sobel, Michael E. 2012. “Does Marriage Boost Men’s Wages? Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data.” Journal of the American Statistical Association 107 (498): 521–9.Google Scholar
Vansteelandt, Sijn. 2009. “Estimating Direct Effects in Cohort and Case–Control Studies.” Epidemiology 20 (6): 851–60.Google Scholar
Vansteelandt, Stijn, and Joffe, Marshall. 2014. “Structural Nested Models and G-estimation: The Partially Realized Promise.” Statistical Science 29 (4): 707–31.Google Scholar
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