Monthly Archives: December 2017

A selection of amazing papers from 2017

As 2017 draws to a close, I am marking the year by posting links to a selection of papers circulated in the past year. These are papers that I read and that I think are especially impressive in moving the methodological frontier forward for people, like myself, who do field experimental and quasi-experimental policy research. I have linked to ungated versions whenever I could:

Foundational Work in Causal Inference

These are papers that challenge conventional wisdom or otherwise newly reveal some basic considerations in causal inference:

  • Pei et al. on when placebo tests are preferred to control [pdf].
  • Abadie et al. on causal standard errors [arxiv].
  • Morgan on permutation vs bootstrap for experimental data [arxiv].
  • Young on how IV inference tends to be overconfident [pdf].
  • Lenz and Sahn on p hacking through covariate control and why we do indeed want to see the zero order correlations [osf].
  • Christian and Barrett on spurious panel IV [pdf].
  • Spiess on the importance of statistical unbiasedness in situations where researchers have to persuade skeptics [pdf].
  • Savje et al. on when we do and don’t need to worry about interference as a first order problem [arxiv].
  • D’amour et al. on a curse of dimensionality for strong ignorability [arxiv].

Generalization, Policy Extrapolation, and Equilibrium Effects

Current program evaluation work is pushing past the old “reduced-form vs. structural” divide and making use of tools from both worlds to address questions of generalization, policy extrapolation, and equilibrium effects. Here are some good recent examples:

  • Muralidharan et al. on equilibrium effects of improving access to a cash for work program in India [pdf].
  • Banerjee et al. on using an experiment and model to infer an optimal strategy to deter drunk driving [pdf].
  • Davis et al on how to design experiments to improve their ability to inform scaled up interventions [nber-gated].

Beautifully Designed Field Studies

Finally, here are papers that I read this year that taught me a lot about framing and designing field experiments. Reading them offers a master class on research design and analysis:

  • Benhassine et al. on the low returns from firm formalization [pdf].
  • Blattman and Dercon on the drawbacks of factory work [ssrn].
  • Munger “bots” experiment to reduce partisan incivility on Twitter [pdf].
  • Green et al experiment on how mass media campaigns can reduce intimate partner violence [pdf].
  • Matz et al on using psychological profiling to target persuasion [pnas].
  • Weigel experiment on how state taxation induces more civic engagement [pdf].