Davide Viviano (Department of Economics), "Policy targeting under interference"
This paper 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 analog 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.
Paper: https://dviviano.github.io/projects/main_text_NEWM_Jan2023.pdf
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LOCATION:CGIS Knafel, room K354 or Online via Zoom STATUS:CONFIRMED DTSTART:20230913T160000Z DTEND:20230913T173000Z END:VEVENT END:VCALENDAR