I’ve been trying to read more carefully about instrumental variables and make up my mind about when IV arguments are scientifically convincing.

Here's a tension I keep running into:

Should the scientific question alone determine the causal parameter of interest?

Or is it legitimate for the target parameter to reflect an interplay between scientific interest and the identifying assumptions we actually find tenable?

IVs can be difficult to interpret when instruments are weak, who “compliers” are is opaque, exclusion restrictions are debatable, or linear models are used in settings where the true data-generating process may be nonlinear.

On the other hand, when an entire body of (aspirationally causal) literature rests on methods that try to close backdoor paths, IVs offer a genuinely different identification strategy. That seems valuable for evidence triangulation, even if IV analyses have their criticisms.

What do you think? Are you a big IV proponent? Are you an IV critic?

When do you find IV evidence persuasive?

Some literature I've been reading & re-reading:

https://pubmed.ncbi.nlm.nih.gov/16755261/

https://academic.oup.com/ije/article/47/4/1289/3095892

https://pmc.ncbi.nlm.nih.gov/articles/PMC4285626/

https://arxiv.org/abs/2402.09332

https://arxiv.org/abs/2402.05639

#CausalInference #InstrumentalVariables #Econometrics #Statistics #DataScience #HealthPolicy

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UMass Chan Medical School

Decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference and #MachineLearning as a #postdoc! No biomed bg needed.

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https://jobrxiv.org/job/umass-chan-medical-school-27778-postdoc-in-single-cell-and-spatial-multi-omic-gene-regulatory-networks/?fsp_sid=11689

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https://doi.org/10.1016/j.socscimed.2017.12.005

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