The causal interpretation of estimated associations in regression models
By Luke Keele, Randolph Stevenson, Felix Elwert. Political Science Research and Methods, 2019
181 citations recorded by the source corpus.
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.
Subfields: Political Science and International Relations, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f240cd5f0338db04149f410a48664d19d6d5361c
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In Sociology and Political Science
In Statistics and Probability
Citations in the corpus
- On the Nuisance of Control Variables in Regression Analysis (2020, background): “This is because even valid controls are often correlated with other unobserved factors, which render their marginal effects uninterpretable from a causal inference perspective (Westreich and Greenland, 2013; Keele et al., 2020).”