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 discontinu...

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Main Authors: Ioana Marinescu, Sofia Triantafillou, Konrad Kording
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0276755
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author Ioana Marinescu
Sofia Triantafillou
Konrad Kording
author_facet Ioana Marinescu
Sofia Triantafillou
Konrad Kording
author_sort Ioana Marinescu
collection DOAJ
description 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 Marginal Threshold Treatment Effect (MTTE), which shows how marginal changes in the threshold can affect the LATE. We extend this idea to define the problem of optimizing a policy threshold, i.e. selecting a threshold that optimizes the cumulative effect of the treatment on the treated. We present an estimator of the optimal threshold based on a constrained optimization framework. We show how to use machine learning (Gaussian process regression) for non-linear estimation. We also extend the estimation to a conservative threshold that is unlikely to produce harm, and we show how to include policy cost constraints. We apply these results to estimate an optimal tip-maximizing threshold for tip suggestions in taxi cabs Haggag (2014).
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spelling doaj.art-035bfe37540943b681eeadb46beb57702022-12-22T04:36:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711e027675510.1371/journal.pone.0276755Regression discontinuity threshold optimization.Ioana MarinescuSofia TriantafillouKonrad KordingTreatments 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 Marginal Threshold Treatment Effect (MTTE), which shows how marginal changes in the threshold can affect the LATE. We extend this idea to define the problem of optimizing a policy threshold, i.e. selecting a threshold that optimizes the cumulative effect of the treatment on the treated. We present an estimator of the optimal threshold based on a constrained optimization framework. We show how to use machine learning (Gaussian process regression) for non-linear estimation. We also extend the estimation to a conservative threshold that is unlikely to produce harm, and we show how to include policy cost constraints. We apply these results to estimate an optimal tip-maximizing threshold for tip suggestions in taxi cabs Haggag (2014).https://doi.org/10.1371/journal.pone.0276755
spellingShingle Ioana Marinescu
Sofia Triantafillou
Konrad Kording
Regression discontinuity threshold optimization.
PLoS ONE
title Regression discontinuity threshold optimization.
title_full Regression discontinuity threshold optimization.
title_fullStr Regression discontinuity threshold optimization.
title_full_unstemmed Regression discontinuity threshold optimization.
title_short Regression discontinuity threshold optimization.
title_sort regression discontinuity threshold optimization
url https://doi.org/10.1371/journal.pone.0276755
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AT sofiatriantafillou regressiondiscontinuitythresholdoptimization
AT konradkording regressiondiscontinuitythresholdoptimization