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DTSTART:20241103T020000
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DESCRIPTION:NOTE: BIS 525 students are required to attend in person. Other
 s are invited to attend in person\, but may also attend via Zoom. SPEAKER
  : Fei Fang\, PhD\, Post Doctoral Associate\, Department of Biostatistics
 \, Yale University TITLE : “Design-Based Weighted Regression Estimators f
 or Average and Conditional Spillover Effects" ABSTRACT: In this paper\, w
 e conceptualize general spillover estimands as weighted sums of unit-to-u
 nit spillover effects with estimand-specific weights under partial interf
 erence. Building on these estimands\, we develop design-based weighted le
 ast squares (WLS) estimators for both average and conditional spillover e
 ffects. Regression-type estimators are appealing because they are intuiti
 ve to construct\, straightforward to implement\, and—when carefully desig
 ned—can yield valid inference even when the underlying outcome structure 
 is complex. For the average-type estimands\, we introduce three construct
 ions of the estimators—the dyadic\, sender\, and receiver perspectives—wh
 ich distribute the estimand weights differently across the outcome vector
 \, design matrix\, and weight matrix. We show that all three perspective 
 estimators are equivalent to the Hájek estimator. To extend this framewor
 k to conditional spillover effects\, we construct parametric WLS estimato
 rs and we establish conditions under which they are consistent for the ta
 rget conditional spillover effects. We further derive concentration inequ
 alities\, a central limit theorem\, and conservative variance estimators 
 for the three perspective estimators in an asymptotic regime where both t
 he number of clusters and cluster sizes grow\, thereby providing a unifie
 d theoretical framework for regression-based spillover estimation under p
 artial interference. We use simulation studies to evaluate the performanc
 e of the proposed estimators for average spillover effects and to examine
  the strength of the theoretical conditions linking the different estimat
 ors to the conditional spillover estimands. Finally\, we demonstrate the 
 utility of our methods by re-analyzing a randomized experiment by Cai et 
 al. (2015)\, which studied the conditional spillover effects of an inform
 ation session on the uptake of weather insurance among rice farmers in Ch
 ina. YSPH values inclusion and access for all participants. If you have q
 uestions about accessibility or would like to request an accommodation\, 
 please contact Charmila Fernandes at Charmila.fernandes@yale.edu . We wil
 l try to provide accommodations requested by November 24\, 2025.\n\nSpeak
 er:\nFei Fang\n\nAdmission:\nFree\n\nDetails URL:\nhttps://medicine.yale.
 edu/event/ysph-biostatistics-seminar-tba-12-2-25-copy/\n
DTEND;TZID=America/New_York:20251202T125000
DTSTAMP:20260701T044851Z
DTSTART;TZID=America/New_York:20251202T120000
GEO:41.302961;-72.931638
LOCATION:106-A&B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar: “Design-Based Weighted Regression Esti
 mators for Average and Conditional Spillover Effects"
UID:c8cf66e5-4e95-4f6a-afc6-ac14b285f8b7
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