Credibility Toryism: Causal Inference, Research Design, and Evidence
by Justin Esarey
I’m now posting my methodological ramblings on The Political Methodologist; check out a post here!
In a prior post on my personal blog, I argued that it is misleading to label matching procedures as causal inference procedures (in the Neyman-Rubin sense of the term). My basic argument was that the causal quality of these inferences depends on untested (and in some cases untestable) assumptions about the matching procedure itself. A regression model is also a “causal inference” model if various underlying assumptions are met, with one primary difference being that regression depends on linearity of the response surface while matching does not. Presumably, regression will be more efficient than matching if this assumption is correct, but less accurate if it is not.
So, if I don’t think that causal inferences come out of a particular research design or model, where do I think they come from?
Let’s step back for a moment. Research designs and statistical models are designed to allow us to surmount…
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