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Seven days have flown by; here are this week’s links:

1. In , Kylie Hutchinson offers some pithy tips on developing a post-evaluation action plan, with links to resources.

2. Taken from the , Rachel Glennerster . Echoing Geoffrey Rose, she notes the importance of effect at scale: ‘it is far better to achieve a 10% improvement for 1 million people than a 50% improvement for 1,000’. This idea reflects the original — epidemiological — use of ‘impact’ (as opposed to effect), which is a function of the effect of an exposure and its prevalence, that has been lost in the modern use in ‘impact evaluations’ used to estimate programme effects.

3. Speaking of effects, Judea Pearl and Dana Mackenzie have a relatively new book out, The Book of Why, . Pearl and colleagues have spent the last few decades developing a complete language of causal analysis. Writing for computer scientists and mathematicians (as well as public health researcher, sometimes), their published academic work can be quite impenetrable, so it’s exciting to see them produce an introductory book.

4. In , three evaluation experts reflect on how equity can be a leading principle in evaluation. There are helpful links to further reading at the end.

5. Finally: can statistics indict President Trump? Maybe: the authors  use simulations to show that it is unlikely (given their model) that the payments we know were paid to Stormy Daniels »å¾±»å²Ô’t come from the Trump campaign (note the double negative). How is this related to evaluation? Tenuously; but it reminded me of a an, by Marie-Claude Boily et al., that used mathematical models to investigate the plausibility that observed changes in the prevalence of HIV at antenatal clinics was due to interventions with sex workers in Karnataka, India. Even with ‘optimistic prevention parameters’ their results suggested that the changes couldn’t be entirely due to the sex-worker interventions. I’ve not seen many examples of this kind of plausibility testing with mathematical models.

Have a good weekend!