Bias mitigation in empirical peace and conflict studies: A short primer on posttreatment variables
| Published date | 01 May 2024 |
| DOI | http://doi.org/10.1177/00223433221145531 |
| Author | Christoph Dworschak |
| Date | 01 May 2024 |
https://doi.org/10.1177/00223433221145531
Journal of Peace Research
2024, Vol. 61(3) 462 –476
© The Author(s) 2023
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DOI: 10.1177/00223433221145531
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1225162JPR0010.1177/00223433221145531Journal of Peace ResearchDworschak
research-article2023
Regular Article
Bias mitigation in empirical peace
and conflict studies: A short primer
on posttreatment variables
Christoph Dworschak
Department of Politics, University of York
Abstract
Posttreatment variables are covariates that are preceded by the main explanatory variable. Their inclusion in a
statistical model does not ‘control’ for their influence on the relationship of interest, and it does not substitute for
a mediation analysis. Likewise, a coefficient estimate of an appropriate ‘control variable’ cannot be interpreted as a
causal effect estimate. While these facts are well-established in various fields across the social sciences, their recog-
nition in the field of peace and conflict studies is more limited. Originally collected data on recent publications from
leading peace and conflict journals reveal that a large majority of evaluated articles condition on posttreatment
variables, demonstrating how a review of these fallacies can help to substantially improve future research on peace and
conflict. Drawing on a broad set of literature and using graphical approaches, I offer an intuitive explanation of the
logic of posttreatment variables and clarify common misconceptions. Building on recent developments in metho-
dology and software, and by deriving conditions for bounding using analytical bias expressions, I discuss avenues for
dealing with posttreatment variables in observational studies. The article concludes with a discussion of implications
for applied research.
Keywords
causal inference, model specification, peace and conflict, political analysis, posttreatment bias
Introduction
Which variables should researchers not condition on (not
‘control for’) in empirical research on peace and conflict?
Research design and variable selection are areas in which
there are no easy answers available. While the computa-
tion of a regression is usually just one click away, which
covariates to include in that regression no computer can
tell (King, Keohane & Verba, 1994).
1
Therefore,
questions of designing research and selecting variables
have been studied abundantly. This article attempts to
raise renewed awareness to the challenge of variable
selection and discusses avenues to address common
issues that are particularly relevant to applied research.
In doing so, emphasis is given to straightforward and
accessible explanations rather than to statistical depth.
The target audience of this article are empirical peace
and conflict researchers.
Most quantitative research on peace and conflict seeks
to approximate causal claims using observational data.
While observational research designs can never match
the gold standard of design-based inference, the use of
appropriate statistical methods, availability of high-
quality data and careful model design can go a long way.
The practice of using ‘control variables,’ that is, of con-
ditioning on covariates to par tial out the effect of an
Corresponding author:
christoph.dworschak@york.ac.uk
1
This and other generalizing statements in this article assume a
deductive quantitative research design seeking to approximate
causal claims in the context of an observational null-hypothesis
significance testing framework. While the lessons drawn here
equally apply to other empirical approaches, including experiments
and qualitative comparison, I adopt a more targeted language due to
scope constraints and to improve accessibility.
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