Additional Reading Suggestions

Life did not stop when we finished the manusdcript. Actually, we keep finding great stuff. So let us make some suggestion for additional readings per chapters.

Part I

Chapter 01

  • On surveys, a great review is “How to run survey: A guide to creating your own identifying variation and revelealing the invisible”, NBER DP Stefanie Stantcheva.

Chapter 06

  • On p-hacking, a fantastic story is about a body of research in social psychology written up in New York Times Magazine in 2017. The review of methods started in 2012 soon led to the birth of data investigation team Data Colada in 2013 by Profs Uri Simonsohn, Leif Nelson and Joe Simmons. They also wrote a paper on p-curve, a tool to analyze a body of literature. Read any other pieces of Data Colada on challenges to reproducibility. Amazing stuff.

Part II

Chapter 09

  • Regarding external validity, one way to check robustness is to take out 1% of the data and repeat the exercise. The simple take is to do it many times randonly + many times by edge of distribution of key variables. The smart take is suggested by Tamara Broderick, Ryan Giordano, Rachael Meager in “An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?” Hard-core statistics. Preprint

Part III

Chapter 16

Part IV

Chapter 19

  • On DAGs and Potential outcomes, deep discussion for social scientists: Imbens, Guido W. 2020. “Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics.” Journal of Economic Literature, 58 (4): 1129-79. LINK to paper. An amazing review that includes Twitter quotes.

Chapter 19

Chapter 20

  • On A/B testing, some neat ideas in presentation by Harlan Harris, with code in R