spdrnl reviewed Effect by Nick Huntington-Klein
None
4 stars
It has been hard to get a good overview of quasi-experimental study designs. Unlike random-control group designs, stemming from medicine, quasi experimental designs stem from different disciplines.
Quasi-experimental study designs aim to tease out causal effects in observational settings. Where random-control group designs have strong internal validity, quasi-experimental designs often have stronger external validity. For those interested, look up "Qausi-experimental study designs - paper 2: Complementary approaches to advancing global health knowledge" By Geldsetzer and Fawzi (2017).
This book first gives a step-by-step introduction of regression methods, and some do-calculus, before progressing to describing thoroughly a large set of quasi-experimental methods. As it often goes, the topic is endless, so although the set is considerable, it is by no means exhaustive; and the author makes no such claim. The tone of the book is very informal (which does not add to brevity), and provides examples in Python, R and Stata. …
It has been hard to get a good overview of quasi-experimental study designs. Unlike random-control group designs, stemming from medicine, quasi experimental designs stem from different disciplines.
Quasi-experimental study designs aim to tease out causal effects in observational settings. Where random-control group designs have strong internal validity, quasi-experimental designs often have stronger external validity. For those interested, look up "Qausi-experimental study designs - paper 2: Complementary approaches to advancing global health knowledge" By Geldsetzer and Fawzi (2017).
This book first gives a step-by-step introduction of regression methods, and some do-calculus, before progressing to describing thoroughly a large set of quasi-experimental methods. As it often goes, the topic is endless, so although the set is considerable, it is by no means exhaustive; and the author makes no such claim. The tone of the book is very informal (which does not add to brevity), and provides examples in Python, R and Stata.
Causal methods become relevant for data scientists if one wants to move past predictive modeling, and wants to explore what measure effectively influence behavior.
What would have made this book for me perfect is Bayesian modeling. The different designs now rely on a wild scattering of statistical packages in for example Python. A lot of these methods could also be executed using a more general Bayesian package, harmonizing and better illuminating the differences and commonalities between the methods.
All-in-all a very good read.