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r-causal / causal-inference-in-R

Causal Inference in R: A book!
https://www.r-causal.org/
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What to control for #25

Open LucyMcGowan opened 3 years ago

LucyMcGowan commented 3 years ago

Here is a nice paper on conditioning on instruments: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3254160/ top line result:

The results indicate that effect estimates which are conditional on a perfect IV or near-IV may have larger bias and variance than the unconditional estimate. However, in most scenarios considered, the increases in error due to conditioning were small compared with the total estimation error. In these cases, minimizing unmeasured confounding should be the priority when selecting variables for adjustment, even at the risk of conditioning on IVs.

LucyMcGowan commented 3 years ago

Also this: https://dash.harvard.edu/bitstream/handle/1/25207409/90937280.pdf?sequence=2&isAllowed=y

LucyMcGowan commented 2 years ago

Ding, Peng, and Luke W. Miratrix. 2015. “To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias.” Journal of Causal Inference 3 (1) (January 1). doi:10.1515/jci-2013-0021.

malcolmbarrett commented 2 years ago

what if p. 191

LucyMcGowan commented 2 years ago

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166439/

LucyMcGowan commented 2 years ago

Wooldridge (2009) and Pearl (2010) have shown that when bias due to unmeasured confounding is present, control for an instrument can amplify the existing confounding bias.

malcolmbarrett commented 2 years ago

Updated the title on this. We now have a short section addressing this topic but should make sure we've covered everything