Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T04:21:21.280Z Has data issue: false hasContentIssue false
  • English
  • Français

The causal interpretation of estimated associations in regression models

Published online by Cambridge University Press:  25 July 2019

Luke Keele ,
Randolph T. Stevenson  and
Felix Elwert
Luke Keele*
Affiliation:
Georgetown University, Washington D.C. 19130, United States
Randolph T. Stevenson
Affiliation:
Department of Political Science, Rice University, P.O. Box 1892, MS-24, Houston, TX 77251, United States
Felix Elwert
Affiliation:
Department of Sociology, University of Wisconsin-Madison, 4426 Sewell Social Sciences, Madison, WI 53706, United States
*
*Corresponding author. E-mail: luke.keele@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

A common causal identification strategy in political science is selection on observables. This strategy assumes one observes a set of covariates that is, after statistical adjustment, sufficient to make treatment status as-if random. Under adjustment methods such as matching or inverse probability weighting, coefficients for control variables are treated as nuisance parameters and are not directly estimated. This is in direct contrast to regression approaches where estimated parameters are obtained for all covariates. Analysts often find it tempting to give a causal interpretation to all the parameters in such regression models—indeed, such interpretations are often central to the proposed research design. In this paper, we ask when we can justify interpreting two or more coefficients in a regression model as causal parameters. We demonstrate that analysts must appeal to causal identification assumptions to give estimates causal interpretations. Under selection on observables, this task is complicated by the fact that more than one causal effect might be identified. We show how causal graphs provide a framework for clearly delineating which effects are presumed to be identified and thus merit a causal interpretation, and which are not. We conclude with a set of recommendations for how researchers should interpret estimates from regression models when causal inference is the goal.

Keywords

Causal inference
Type
Original Articles
Information
Political Science Research and Methods , Volume 8 , Issue 1 , January 2020 , pp. 1 - 13
Copyright
Copyright © The European Political Science Association 2019 

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.)

References

Acharya, A, Blackwell, M and Sen, M (2016) Explaining causal findings without bias: detecting and assessing direct effects. American Political Science Review 110, 512–529.Google Scholar
Campbell, A, Converse, PE, Miller, WE and Donald, E (1966) Stokes. 1960. The American Voter. New York: Wiley.Google Scholar
Carsey, TM and Layman, GC (2006) Changing sides or changing minds? Party identification and policy preferences in the American electorate. American Journal of Political Science 50, 464–477.Google Scholar
Cox, GW (1997) Making Votes Count: Strategic Coordination in the World's Electoral Systems. Cambridge, UK: Cambridge University Press.Google Scholar
Duverger, M (1959) Political Parties: Their Organization and Activity in the Modern State. New York, NY: Methuen.Google Scholar
Elwert, F (2013) Graphical causal models. In Stephen, L. Morgan (ed). Handbook of Causal Analysis for Social Research. Amsterdam: Springer, pp. 245–273.Google Scholar
Fiorina, MP (1981) Retrospective voting in American national elections.Google Scholar
Gibler, DM (2017) State development, parity, and international conflict. American Political Science Review 111, 21–38.Google Scholar
Glynn, AN and Quinn, KM (2010) An introduction to the augmented inverse propensity weighted estimator. Political Analysis 18, 36–56.Google Scholar
Goren, P and Chapp, C (2017) Moral power: how public opinion on culture war issues shapes partisan predispositions and religious orientations. American Political Science Review 111, 159–177.Google Scholar
Green, DP and Palmquist, B (1994) How stable is party identification? Political Behavior 16, 437–466.Google Scholar
Green, DP, Palmquist, B and Schickler, E (2004) Partisan Hearts and Minds: political Parties and the Social Identities of Voters. New Haven, Conn: Yale University Press.Google Scholar
Gulzar, S and Pasquale, BJ (2017) Politicians, bureaucrats, and development: evidence from India. American Political Science Review 111, 162–183.Google Scholar
Hainmueller, J and Hazlett, C (2013) Kernel regularized least squares: reducing Misspecification bias with a flexible and interpretable machine learning approach. Political Analysis 22, 143–168.Google Scholar
Highton, B and Kam, CD (2011) The long-term dynamics of partisanship and issue orientations. The Journal of Politics 73, 202–215.Google Scholar
Ho, DE, Imai, K, King, G and Stuart, EA (2007) Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political analysis 15, 199–236.Google Scholar
Imai, K, Keele, L, Tingley, D and Yamamoto, T (2011) Unpacking the black box of causality: learning about causal mechanisms from experimental and observational studies. American Political Science Review 105, 765–789.Google Scholar
Keele, LJ (2015) The statistics of causal inference: a view from political methodology. Political Analysis 23, 313–335.Google Scholar
Kim, IS (2017) Political cleavages within industry: firm-level lobbying for trade liberalization. American Political Science Review 111, 1–20.Google Scholar
Klasnja, M and Titiunik, R (2017) The incumbency curse: weak parties, term limits, and unfulfilled accountability. American Political Science Review 111, 129–148.Google Scholar
MacKuen, MB, Erikson, RS and Stimson, JA (1989) Macropartisanship. American Political Science Review 83, 1125–1142.Google Scholar
Miller, WE and Shanks, JM (1996) The New American Voter. Cambridge, MA: Harvard University Press.Google Scholar
Morgan, SL and Winship, C (2014) Counterfactuals and Causal Inference: Methods and Principles for Social Research. 2nd Edn. New York, NY: Cambridge University Press.Google Scholar
Ordeshook, PC and Shvetsova, OV (1994) Ethnic heterogeneity, district magnitude, and the number of parties. American Journal of Political Science 38, 100–123.Google Scholar
Page, BI and Jones, CC (1979) Reciprocal effects of policy preferences, party loyalties and the vote. American Political Science Review 73, 1071–1089.Google Scholar
Pearl, J (2009 a) Causal inference in statistics: an overview. Statistics Surveys 3, 96–146.Google Scholar
Pearl, J (2009 b) Causality: Models, Reasoning, and Inference. 2nd Edn. New York: Cambridge University Press.Google Scholar
Powell, GB (1982) Contemporary Democracies. Cambridge, MA: Harvard University Press.Google Scholar
Przeworski, A and Sprague, J (1986) Paper Stones: A History of Electoral Socialism. Chicago, IL: University of Chicago Press.Google Scholar
Taagepera, R and Shugart, MS (1989) Seats and Votes: The Effects and Determinants of Electoral Systems. New Haven, CT: Yale University Press.Google Scholar
Touchton, M, Sugiyama, NB and Wampler, B (2017) Democracy at work: moving beyond elections to improve well-being. American Political Science Review 111, 68–82.Google Scholar
Van der Weele, TJ (2009) On the distinction between interaction and effect modification. Epidemiology 20, 863–871.Google Scholar
Van der Weele, TJ (2015) Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford, UK: Oxford University Press.Google Scholar
Van der Weele, TJ, Hernán, MA and Robins, JM (2008) Causal directed acyclic graphs and the direction of unmeasured confounding bias. Epidemiology (Cambridge, Mass.) 19, 720.Google Scholar