Regression discontinuity threshold optimization

Treatments often come with thresholds, e.g. we are given statins if our cholesterol is above a certain threshold. But which statin administration threshold maximizes our quality of life adjusted years? More generally, which threshold would optimize the average expected outcome? Regression discontinuity approaches are used to measure the local average treatment effect (LATE) and more recently also the treatment effect derivative (TED). Here we show how they can be used to optimize the threshold itself using linear methods related to Newton's method as well as Gaussian process regressions. Phrasing the problem as optimization allows for a range of distinct estimators, including one that is unlikely to produce harm.

[1]  Milton C Weinstein,et al.  Cost-effectiveness of 10-Year Risk Thresholds for Initiation of Statin Therapy for Primary Prevention of Cardiovascular Disease. , 2015, JAMA.

[2]  Loretta J. Mester What's the point of credit scoring? , 1997 .

[3]  Luke W. Miratrix,et al.  A nonparametric Bayesian methodology for regression discontinuity designs , 2017, Journal of Statistical Planning and Inference.

[4]  D. Campbell,et al.  Regression-Discontinuity Analysis: An Alternative to the Ex-Post Facto Experiment , 1960 .

[5]  Jianqing Fan Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66 , 1996 .

[6]  Yingying Dong Alternative Assumptions to Identify LATE in Fuzzy Regression Discontinuity Designs , 2018, Oxford Bulletin of Economics and Statistics.

[7]  Ben Ost,et al.  The Returns to College Persistence for Marginal Students: Regression Discontinuity Evidence from University Dismissal Policies , 2017, Journal of Labor Economics.

[8]  Till Bärnighausen,et al.  Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. , 2015, Journal of clinical epidemiology.

[9]  Jackson T. Wright,et al.  2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). , 2014, JAMA.

[10]  J. Heckman,et al.  Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation , 2007 .

[11]  Luca Vogt Statistics For Spatial Data , 2016 .

[12]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[13]  H. Wackernagle,et al.  Multivariate geostatistics: an introduction with applications , 1998 .

[14]  Andrew Gelman,et al.  Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs , 2014 .

[15]  Martin Wattenberg,et al.  Ad click prediction: a view from the trenches , 2013, KDD.

[16]  David S. Lee,et al.  Regression Discontinuity Designs in Economics , 2009 .

[17]  Arthur Lewbel,et al.  Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models , 2015, Review of Economics and Statistics.

[18]  Nicholas I. M. Gould,et al.  Trust Region Methods , 2000, MOS-SIAM Series on Optimization.

[19]  Kareem Haggag,et al.  Default Tips , 2014 .

[20]  J. Hahn,et al.  IDENTIFICATION AND ESTIMATION OF TREATMENT EFFECTS WITH A REGRESSION-DISCONTINUITY DESIGN , 2001 .

[21]  Sebastian Calonico,et al.  Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs , 2014 .

[22]  Joshua D. Angrist,et al.  Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff , 2015 .