Policy Targeting under Network Interference.
This article studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analogue of average social welfare when spillovers occur. I construct semi-parametric welfare estimato...
| Publicado en: | Review of Economic Studies Vol. 92; no. 2; pp. 1257 - 1293 |
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| Formato: | Artículo |
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Oxford University Press / USA
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184192953&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184192953 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: Mar2025 vid: 92 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184192953 10.1093/restud/rdae041 ppf: 1257 ppct: 36 formats: tig: atl: Policy Targeting under Network Interference. aug: au: Viviano, Davide affil: Department of Economics, Harvard University, USA su: Social services Mixed integer linear programming Treatment effect heterogeneity Causal inference Information networks sug: subj: Social services Other Individual and Family Services Mixed integer linear programming Treatment effect heterogeneity Causal inference Information networks keyword: Social interactions Spillovers Welfare maximization Social interactions Spillovers Welfare maximization ab: This article studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analogue of average social welfare when spillovers occur. I construct semi-parametric welfare estimators with known and unknown propensity scores and cast the optimization problem into a mixed-integer linear program, which can be solved using off-the-shelf algorithms. I derive a strong set of guarantees on regret, i.e. the difference between the maximum attainable welfare and the welfare evaluated at the estimated policy. The proposed method presents attractive features for applications: (i) it does not require network information of the target population; (ii) it exploits heterogeneity in treatment effects for targeting individuals; (iii) it does not rely on the correct specification of a particular structural model; and (iv) it accommodates constraints on the policy function. An application for targeting information on social networks illustrates the advantages of the method. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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