Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T15:19:31.814Z Has data issue: false hasContentIssue false
  • English
  • Français

Flexible Causal Inference for Political Science

Published online by Cambridge University Press:  02 January 2018

Bear F. Braumoeller ,
Giampiero Marra ,
Rosalba Radice  and
Aisha E. Bradshaw
Bear F. Braumoeller*
Affiliation:
Department of Political Science, The Ohio State University, Columbus, OH 43210, USA. Email: [email protected]
Giampiero Marra
Affiliation:
Department of Statistical Science, University College, Gower Street, London WC1E 6BT, UK
Rosalba Radice
Affiliation:
Department of Economics, Mathematics and Statistics, Birkbeck University of London, Malet Street, London WC1E 7HX, UK
Aisha E. Bradshaw
Affiliation:
Department of Political Science, The Ohio State University, Columbus, OH 43210, USA. Email: [email protected]
*
*Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of political science, for two reasons: behavior is generally endogenous, and the threat of unobserved variables that confound the relationship between behavior and outcomes is pervasive. Matching methods, widely considered to be the state of the art in causal inference in political science, are generally ill-suited to inference in the presence of unobserved confounders. Heckman-style multiple-equation models offer a solution to this problem; however, they rely on functional-form assumptions that can produce substantial bias in estimates of average treatment effects. We describe a category of models, flexible joint likelihood models, that account for both features of the data while avoiding reliance on rigid functional-form assumptions. We then assess these models’ performance in a series of neutral simulations, in which they produce substantial (55% to ${>}$90%) reduction in bias relative to competing models. Finally, we demonstrate their utility in a reanalysis of Simmons’ (2000) classic study of the impact of Article VIII commitment on compliance with the IMF’s currency-restriction regime.

Keywords

causalitysemiparametric estimationaverage treatment effectsrecursive methodsinternational institutionsinstrumental variables
Type
Articles
Information
Political Analysis , Volume 26 , Issue 1 , January 2018 , pp. 54 - 71
Copyright
Copyright © The Author(s) 2017. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Authors’ note: The authors are grateful to Janet Box-Steffensmeier, Justin Esarey, William Minozzi, and the participants in the Ohio State University Research in International Politics workshop, Emory University’s Political Institutions and Methodology seminar series, and the Rice University Symposium on Agreements, Law, and International Politics for helpful comments. R code and data for both the simulations and empirical analyses can be found on the Political Analysis Dataverse (Braumoeller et al.2017).

Contributing Editor: R. Michael Alvarez

References

Angrist, Joshua, and Krueger, Alan B.. 2001. Instrumental variables and the search for identification: From supply and demand to natural experiments. Technical report National Bureau of Economic Research.Google Scholar
Bagozzi, B. E., Hill, D. W., Moore, W. H., and Mukherjee, B.. 2015. Modeling two types of peace: The zero-inflated ordered probit (ZiOP) model in conflict research. Journal of Conflict Resolution 59(4):728–752.Google Scholar
Bartels, Larry M. 2000. Partisanship and voting behavior, 1952–1996. American Journal of Political Science 44(1):35.Google Scholar
Bausch, Andrew W. 2015. Democracy, war effort, and the systemic democratic peace. Journal of Peace Research 52(4):435–447.Google Scholar
Bearce, David H., Flanagan, Kristen M., and Floros, Katharine M.. 2006. Alliances, internal information, and military conflict among member-states. International Organization 60(3):595–625.Google Scholar
Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard. 1998. Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American journal of Political Science 42(4):1260–1288.Google Scholar
Benson, Brett V. 2011. Unpacking alliances: Deterrent and compellent alliances and their relationship with conflict, 1816–2000. Journal of Politics 73(4):1111–1127.Google Scholar
Blomberg, S. Brock, Hess, Gregory D., and Weerapana, Akila. 2004. Economic conditions and terrorism. European Journal of Political Economy 20(2):463–478.Google Scholar
Boehmke, Frederick J., Morey, Daniel S., and Shannon, Megan. 2006. Selection bias and continuous-time duration models: Consequences and a proposed solution. American Journal of Political Science 50(1):192–207.Google Scholar
Braumoeller, B. F., and Carson, A.. 2011. Political irrelevance, democracy, and the limits of militarized conflict. Journal of Conflict Resolution 55(2):292–320.Google Scholar
Braumoeller, Bear, Marra, Giampiero, Radice, Rosalba, and Bradshaw, Aisha. 2017. Replication data for: Flexible causal inference for political science. doi:10.7910/DVN/DXDQV3.Google Scholar
Bueno de Mesquita, Bruce, Morrow, James D., Siverson, Randolph M., and Smith, Alastair. 1999. An institutional explanation of the democratic peace. American Political Science Review 93(4):791–807.Google Scholar
Carrubba, Clifford J., Yuen, Amy, and Zorn, Christopher. 2007. In defense of comparative statics: Specifying empirical tests of models of strategic interaction. Political Analysis 15(04):465–482.Google Scholar
Chib, S., and Greenberg, E.. 2007. Semiparametric modeling and estimation of instrumental variable models. Journal of Computational and Graphical Statistics 16:86–114.Google Scholar
Chiba, Daina, Martin, Lanny, and Stevenson, Randy. 2015. A copula approach to the problem of selection bias in models of government survival. Political Analysis 23:42–58.Google Scholar
Chiba, Daina, Metternich, Nils W., and Ward, Michael D.. 2015. Every story has a beginning, middle, and an end (but not always in that order): Predicting duration dynamics in a unified framework. Political Science Research and Methods 3(3):515–541.Google Scholar
Clarke, Kevin A. 2007. A simple distribution-free test for nonnested hypotheses. Political Analysis 15(3):347–363.Google Scholar
Cranmer, Skyler J., Desmarais, Bruce A., and Menninga, Elizabeth J.. 2012. Complex dependencies in the alliance network. Conflict Management and Peace Science 29(3):279–313.Google Scholar
Dorussen, H., and Ward, H.. 2008. Intergovernmental organizations and the kantian peace: A network perspective. Journal of Conflict Resolution 52(2):189–212.Google Scholar
Doyle, Michael W., and Sambanis, Nicholas. 2000. International peacebuilding: A theoretical and quantitative analysis. The American Political Science Review 94(4):779.Google Scholar
Fang, Songying, Johnson, Jesse C., and Leeds, Brett Ashley. 2014. To concede or to resist? The restraining effect of military alliances. International Organization 68(4):775–809.Google Scholar
Freedman, D. A., and Sekhon, J. S.. 2010. Endogeneity in probit response models. Political Analysis 18(2):138–150.Google Scholar
Freedman, David, Collier, David, Sekhon, Jasjeet Singh, and Stark, Philip B.. 2010. Statistical models and causal inference: A dialogue with the social sciences . Cambridge; New York: Cambridge University Press.Google Scholar
Freytag, Andreas, Krüger, Jens J., Meierrieks, Daniel, and Schneider, Friedrich. 2011. The origins of terrorism: Cross-country estimates of socio-economic determinants of terrorism. European Journal of Political Economy 27(Supplement 1):S5–S16.Google Scholar
Fukumoto, Kentaro. 2015. What happens depends on when it happens: Copula-based ordered event history analysis of civil war duration and outcome. Journal of the American Statistical Association 110(509):83–92.Google Scholar
Gartner, Scott Sigmund. 2011. Signs of trouble: Regional organization mediation and civil war agreement durability. The Journal of Politics 73(02):380–390.Google Scholar
Gates, Scott, Knutsen, Torbjørn, and Moses, Jonathon W.. 1996. Democracy and peace: A more skeptical view. Journal of Peace Research 33(1):1–10.Google Scholar
Gerber, Alan S., and Green, Donald P.. 2000. The effects of canvassing, telephone calls, and direct mail on voter turnout: A field experiment. American Political Science Review 94(3):653–663.Google Scholar
Gerber, Alan S., and Green, Donald P.. 2005. Correction to Gerber and Green (2000), replication of disputed findings, and reply to Imai (2005). American Political Science Review 99(2):301–313.Google Scholar
Gilligan, Michael J., and Sergenti, Ernest J.. 2008. Do UN interventions cause peace? Using matching to improve causal inference. Quarterly Journal of Political Science 3(2):89–122.Google Scholar
Grier, Kevin B., and Tullock, Gordon. 1989. An empirical analysis of cross-national economic growth, 1951–80. Journal of Monetary Economics 24(2):259–276.Google Scholar
Hafner-Burton, Emilie M., and Kahler, Miles. 2009. Network analysis for international relations. International Organization 63:559–592.Google Scholar
Hill, Daniel W. 2010. Estimating the effects of human rights treaties on state behavior. The Journal of Politics 72(04):1161–1174.Google Scholar
Imai, Kosuke. 2005. Do get-out-the-vote calls reduce turnout? The importance of statistical methods for field experiments. American Political Science Review 99(2):283–300.Google Scholar
Johnson, Jesse C., and Leeds, Brett Ashley. 2011. Defense pacts: A prescription for peace? Foreign Policy Analysis 7(1):45–65.Google Scholar
Keele, Luke. 2015. The statistics of causal inference: A view from political methodology. Political Analysis 23(3):313–335.Google Scholar
King, Gary, and Zeng, Langche. 2001. Logistic regression in rare events data. Political analysis 9(2):137–163.Google Scholar
Kish, Leslie. 1959. Some statistical problems in research design. American Sociological Review 24(3):328–338.Google Scholar
Krieger, Tim, and Meierrieks, Daniel. 2011. What causes terrorism? Public Choice 147(1):3–27.Google Scholar
Levy, Jack S. 1981. Alliance formation and war behavior: An analysis of the great powers, 1495–1975. Journal of Conflict Resolution 25(4):581–613.Google Scholar
Lewis, J. B. 2003. Revealing preferences: Empirical estimation of a crisis bargaining game with incomplete information. Political Analysis 11(4):345–367.Google Scholar
Little, R. J. A. 1985. A note about models for selectivity bias. Econometrica 53:1469–1474.Google Scholar
Lupu, Yonatan. 2013. The informative power of treaty commitment: Using the spatial model to address selection effects. American Journal of Political Science 57(4):912–925.Google Scholar
Manski, Charles F. 1990. Nonparametric bounds on treatment effects. The American Economic Review 80(2):319–323.Google Scholar
Maoz, Zeev. 2009. The effects of strategic and economic interdependence on international conflict across levels of analysis. American Journal of Political Science 53(1):223–240.Google Scholar
Maoz, Zeev, and Russett, Bruce. 1993. Normative and structural causes of democratic peace, 1946–1986. American Political Science Review 87(3):624–638.Google Scholar
Marra, Giampiero, and Radice, Rosalba. 2011. Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity. Canadian Journal of Statistics 39:259–279.Google Scholar
Marra, Giampiero, and Radice, Rosalba. 2015. Flexible bivariate binary models for estimating the efficacy of phototherapy for newborns with Jaundice. International Journal of Statistics and Probability 4:46–58.Google Scholar
Marra, Giampiero, and Radice, Rosalba. 2017a. GJRM: Generalised joint regression modelling. R package version 0.1-2. http://CRAN.R-project.org/package=GJRM.Google Scholar
Marra, Giampiero, and Radice, Rosalba. 2017b. Bivariate copula additive models for location, scale and shape. Computational Statistics & Data Analysis 112:99–113.Google Scholar
Marra, Giampiero, and Wood, Simon N.. 2012. Coverage properties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics 39:53–74.Google Scholar
McLaughlin Mitchell, Sara, and Hensel, Paul R.. 2007. International institutions and compliance with agreements. American Journal of Political Science 51(4):721–737.Google Scholar
Meierrieks, Daniel, and Gries, Thomas. 2012. Causality between terrorism and economic growth. Journal of Peace Research 50(1):91–104.Google Scholar
Monfardini, C., and Radice, R.. 2008. Testing exogeneity in the bivariate probit model: A Monte Carlo study. Oxford Bulletin of Economics and Statistics 70:271–282.Google Scholar
Morgan, Stephen L., and Winship, Christopher. 2007. Counterfactuals and causal inference: Methods and principles for social research Analytical Methods for Social Research. New York: Cambridge University Press.Google Scholar
Murray, Michael P. 2006. Avoiding invalid instruments and coping with weak instruments. Journal of Economic Perspectives 20(4):111–132.Google Scholar
Radice, Rosalba, Marra, Giampiero, and Wojtys, Malgorzata. 2016. Copula regression spline models for binary outcomes. Statistics and Computing 26(5):981–995.Google Scholar
Reuveny, Rafael, and Li, Quan. 2003. The joint democracy-dyadic conflict nexus: A simultaneous equations model. International Studies Quarterly 47(3):325–346.Google Scholar
Sartori, Anne E. 2003. An estimator for some binary-outcome selection models without exclusion restrictions. Political Analysis 11(2):111–138.Google Scholar
Sekhon, Jasjeet S. 2009. Opiates for the matches: Matching methods for causal inference. Annual Review of Political Science 12(1):487–508.Google Scholar
Signorino, Curtis S. 1999. Strategic interaction and the statistical analysis of international conflict. American Political Science Review 93(2):279–297.Google Scholar
Simmons, Beth A. 2000. International law and state behavior: Commitment and compliance in international monetary affairs. The American Political Science Review 94(4):819.Google Scholar
Simmons, Beth A. 2009. Mobilizing for human rights: International law in domestic politics . Cambridge: Cambridge University Press.Google Scholar
Simmons, Beth A., and Hopkins, Daniel J.. 2005. The constraining power of international treaties: Theory and methods. American Political Science Review 99(4):623–631.Google Scholar
Smith, Alastair. 1999. Testing theories of strategic choice: The example of crisis escalation. American Journal of Political Science 43(4):1254–1283.Google Scholar
Theisen, Ole Magnus. 2012. Climate clashes? Weather variability, land pressure, and organized violence in Kenya, 1989–2004. Journal of Peace Research 49(1):81–96.Google Scholar
Tir, Jaroslav, and Stinnett, Douglas M. 2012. Weathering climate change: Can institutions mitigate international water conflict? Journal of Peace Research 49(1):211–225.Google Scholar
von Stein, Jana. 2005. Do treaties constrain or screen? Selection bias and treaty compliance. American Political Science Review 99(04):611–622.Google Scholar
Vreeland, James Raymond. 2003. The IMF and economic development . New York: Cambridge University Press.Google Scholar
Winkelmann, R. 2012. Copula bivariate probit models: with an application to medical expenditures. Health Economics 21:1444–1455.Google Scholar
Winship, Christopher, and Mare, Robert D.. 1992. Models for sample selection bias. Annual Review of Sociology 18:327–350.Google Scholar
Wooldridge, J. M. 2010. Econometric analysis of cross section and panel data . Cambridge: MIT Press.Google Scholar
Xiang, Jun. 2010. Relevance as a latent variable in dyadic analysis of conflict. The Journal of Politics 72(02):484.Google Scholar
Supplementary material: File

Braumoeller et al supplementary material 1

Appendix

Download Braumoeller et al supplementary material 1(File)
File 99.7 KB