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Range: Why Generalists Triumph in a Specialized World is a 2019 book by David Epstein in which he expands on …
A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to …
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and …
This book is a popularized text regarding Pearl's work on causal theory and do calculus. It is remarkable. The book tells the tale of how causal theory has been misunderstood for long in the science of statistics, mostly by the Anglo-Saxon null-hypothesis 'gang' of Fisher, Pearson and Galton; putting in doubt a lot of 20th century social research. The book also joyfully reveals how the commonly regarded 'simple' and 'explainable' linear model has been misapplied most of the times, putting my own doubts at rest.
Accounting started in around 5000 BC in Mesopotamia. It was not until the nineties that Edward Jaynes finalized the concept of Bayesian probability. Around this time Pearl also was making his work known. Statistics in the social sciences is just getting started.
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.
Vignettes from the first Trump presidential transition after firing the transition team and throwing the transition plans out the window.
A chilling, globe-spanning detective story, tracking an elite group of Russian hackers and the future of global warfare
In 2014, …
