  The causal effect of an intervention (treatment/exposure) on an outcome can
be estimated by: i) specifying knowledge about the data-generating process; ii)
assessing under what assumptions a target quantity, such as for example a
causal odds ratio, can be identified given the specified knowledge (and given
the measured data); and then, iii) using appropriate statistical estimation
techniques to estimate the desired parameter of interest. As regression is the
cornerstone of statistical analysis, it seems obvious to ask: is it appropriate
to use estimated regression parameters for causal effect estimation? It turns
out that using regression for effect estimation is possible, but typically
requires more assumptions than competing methods. This manuscript provides a
comprehensive summary of the assumptions needed to identify and estimate a
causal parameter using regression and, equally important, discusses the
resulting implications for statistical practice.
