A Practical Guide to Regression Discontinuity Designs in Political Science

By Christopher Skovron, R. Titiunik. 2015

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “If there are many instruments, and these can arise from a few instruments interacted with indicators for subpopulations, or from other flexible transformations of the basic instrument, standard methods are known to have poor properties (Staiger and Stock [1997]).”
Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs (2014, unknown): “447 July 2019, Vol. 37, No. 3 © 2019 is not plausible; Imbens and Lemieux (2008), Van Der Klaauw (2008), Lee and Lemieux (2010), DiNardo and Lee (2010), and Skovron and Titiunik (2015) provide recent reviews.”
A regression discontinuity design for ordinal running variables: Evaluating central bank purchases of corporate bonds (2019, background): “…local linear regressions or polynomials to extrapolate the counterfactual potential outcome under the opposite treatment status and estimate the causal effects at the threshold (e.g. Hahn, Todd and van der Klaauw, 2001; Imbens and Lemieux, 2008; Skovron and Titiunik, 2015; Gelman and Imbens, 2019).”