Over the summer I’ve been slowly working my way through the new book Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Guido Imbens and Don Rubin. It is an introduction in the sense that it is 600 pages and still doesn’t have room for difference-in-differences, regression discontinuity, synthetic controls, power calculations, dealing with attrition, dealing with multiple time periods, treatment spillovers, or many other topics in causal inference (they promise a volume 2). But not an introduction in that it is graduate level and I imagine would be very confusing if you had no previous exposure to causal inference. So I thought I’d share some thoughts on this book for our readers.
David McKenzie's blog
- Freakonomics podcast transcript covering Chris Blattman’s work on giving grants and cognitive behavioral therapy to men in Liberia, as well as related work in the US and UK
- On the CGD blog, the political paradox of cash transfers
- Rohini Pande and Charity Moore on the puzzle of why India’s women are working less with development, not more – they call for gender quotas in the labor market.
This list is a companion to our curated list on technical topics. It puts together our posts on issues of measurement, survey design, sampling, survey checks, managing survey teams, reducing attrition, and all the behind-the-scenes work needed to get the data needed for impact evaluations.
This is a curated list of our technical postings, to serve as a one-stop shop for your technical reading. I’ve focused here on our posts on methodological issues in impact evaluation – we also have a whole lot of posts on how to conduct surveys and measure certain concepts that I’ll leave for another time. Updated August 20, 2015.
- Nature covers the rise of randomized experiments in development economics
- In VoxEU, Sebastian Galiani and co-authors summarize an experiment in Colombia which, like several other recent experiments, finds reducing the fixed costs to formalization does not do much in terms of getting informal firms to formalize.
- Andrew Gelman on reasons not to trust papers using Mechanical Turk
- Vox covers the difficulty in knowing whether policies work with a description of a study which got the public to guess which programs work or not, and found they do little better than chance – and a tough 10-question quiz where you can see how well you can guess whether U.S. programs work or not.
Household surveys in crisis – Bruce Meyer and co-authors in a new NBER working paper highlight the many issues due to declining cooperation of respondents in the U.S.:
- Unit nonresponse: Households have become increasingly less likely to answer surveys at all: nonresponse rates for major U.S. surveys like the CPS, SIPP, and GSS now exceed 20 percent