Sensitivity analysis cookbook Suppose you are asked to replicate a paper and to perform a sensisitivity analysis. Where would you start from?
The paper is in Economics so the point is to replicate main tables (multivariate regressions, fe regressions, ...) and to run a sensitivity analysis. From my understanding, this would mean to play with controls and functional form to check if results change or not. But paper published in top journal nowadays already include every possible robustness check so I am a bit lost about what I am supposed to do.
I know it's not easy without any background but I am looking for something like a cookbook for running sensitivity analysis, that contains the generic steps one is supposed to follow. 
 A: The following two example papers are from biostatistics (and for clinical trials specifically), but might be a useful starting point. I suspect many of the principles will apply, even if the exact analytical approach might differ for different study designs or research paradigms.
They do spell out some criteria for considering whether a sensitivity analysis is useful, or what additional information might apply, such as: 
"Is it possible for the proposed sensitivity analysis to arrive at a different conclusion to the primary analysis?" (Morris et al.) 
"Will the results change if we take missing data into account? Will the method of handling missing data lead to different conclusions?" (Thabane et al.)

Morris TP, Kahan BC, White IR. Choosing sensitivity analyses for randomised trials: principles. BMC Med Res Methodol 2014;14:11. doi: 10.1186/1471-2288-14-11 
https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-11
Thabane L, Mbuagbaw L, Zhang S, et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol 2013;13:92. doi: 10.1186/1471-2288-13-92
https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-92

EDIT: 24/9
Here's an additional article, which has both an overview of reasons to undertake bias analysis and more technical detail on how these can be implemented.
Lash TL, Fox MP, MacLehose RF, et al. Good practices for quantitative bias analysis. Int J Epidemiol 2014;43(6):1969-85. doi: 10.1093/ije/dyu149
