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Residual Balancing: A Method of Constructing Weights for Marginal Structural Models

Published online by Cambridge University Press:  04 March 2020

Xiang Zhou Open the ORCID record for Xiang Zhou [Opens in a new window]  and
Geoffrey T. Wodtke
Xiang Zhou*
Affiliation:
Department of Sociology, Harvard University, 1737 Cambridge Street, Cambridge, MA02138, USA. Email: [email protected]
Geoffrey T. Wodtke
Affiliation:
Department of Sociology, University of Chicago, 1126 E. 59th St. Chicago, IL60637, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

When making causal inferences, post-treatment confounders complicate analyses of time-varying treatment effects. Conditioning on these variables naively to estimate marginal effects may inappropriately block causal pathways and may induce spurious associations between treatment and the outcome, leading to bias. To avoid such bias, researchers often use marginal structural models (MSMs) with inverse probability weighting (IPW). However, IPW requires models for the conditional distributions of treatment and is highly sensitive to their misspecification. Moreover, IPW is relatively inefficient, susceptible to finite-sample bias, and difficult to use with continuous treatments. We introduce an alternative method of constructing weights for MSMs, which we call “residual balancing”. In contrast to IPW, it requires modeling the conditional means of the post-treatment confounders rather than the conditional distributions of treatment, and it is therefore easier to use with continuous treatments. Numeric simulations suggest that residual balancing is both more efficient and more robust to model misspecification than IPW and its variants in a variety of scenarios. We illustrate the method by estimating (a) the cumulative effect of negative advertising on election outcomes and (b) the controlled direct effect of shared democracy on public support for war. Open-source software is available for implementing the proposed method.

Keywords

causal inferencebalancingpanel data
Type
Articles
Information
Political Analysis , Volume 28 , Issue 4 , October 2020 , pp. 487 - 506
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Jeff Gill

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