Cerqua Augusto, Di Stefano Roberta, Mattera Raffaele
{"title":"The Clustered Dose-Response Function Estimator for continuous treatment with heterogeneous treatment effects","authors":"Cerqua Augusto, Di Stefano Roberta, Mattera Raffaele","doi":"arxiv-2409.08773","DOIUrl":null,"url":null,"abstract":"Many treatments are non-randomly assigned, continuous in nature, and exhibit\nheterogeneous effects even at identical treatment intensities. Taken together,\nthese characteristics pose significant challenges for identifying causal\neffects, as no existing estimator can provide an unbiased estimate of the\naverage causal dose-response function. To address this gap, we introduce the\nClustered Dose-Response Function (Cl-DRF), a novel estimator designed to\ndiscern the continuous causal relationships between treatment intensity and the\ndependent variable across different subgroups. This approach leverages both\ntheoretical and data-driven sources of heterogeneity and operates under relaxed\nversions of the conditional independence and positivity assumptions, which are\nrequired to be met only within each identified subgroup. To demonstrate the\ncapabilities of the Cl-DRF estimator, we present both simulation evidence and\nan empirical application examining the impact of European Cohesion funds on\neconomic growth.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"85 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Many treatments are non-randomly assigned, continuous in nature, and exhibit
heterogeneous effects even at identical treatment intensities. Taken together,
these characteristics pose significant challenges for identifying causal
effects, as no existing estimator can provide an unbiased estimate of the
average causal dose-response function. To address this gap, we introduce the
Clustered Dose-Response Function (Cl-DRF), a novel estimator designed to
discern the continuous causal relationships between treatment intensity and the
dependent variable across different subgroups. This approach leverages both
theoretical and data-driven sources of heterogeneity and operates under relaxed
versions of the conditional independence and positivity assumptions, which are
required to be met only within each identified subgroup. To demonstrate the
capabilities of the Cl-DRF estimator, we present both simulation evidence and
an empirical application examining the impact of European Cohesion funds on
economic growth.