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UID:0-1705@hmc.edu
DTSTART;TZID=America/Los_Angeles:20251202T082500
DTEND;TZID=America/Los_Angeles:20251202T092500
DTSTAMP:20251124T201117Z
URL:https://www.hmc.edu/calendar/events/mathematics-or-candidate-research-
 talk-mayleen-cortez-rodriguez/
SUMMARY:Mathematics OR Candidate Research Talk\, Mayleen Cortez-Rodriguez
DESCRIPTION:Mayleen Cortez-Rodriguez\, a finalist for a tenure-track facult
 y position in the Department of Mathematics\, will deliver the lecture"Est
 imating Causal Effects in the Presence of Interference with No Network Kno
 wledge"\nAbstract\nDo phone call reminders to vote increase voter turnout?
  Does a vaccine decrease disease rates? The answers to causal questions li
 ke these can inform public policy decisions and public health strategies. 
 A broad goal of causal inference\, the field that studies causality\, is t
 o quantify the causal effect of a treatment (e.g. phone call or vaccine) o
 n an outcome (e.g. voter turnout or disease rate) to answer a causal quest
 ion of interest. Randomized Control Trials (RCTs) are considered the “go
 ld standard” for estimating causal effects because they satisfy an impor
 tant independence assumption about the treatment. However\, even RCTs can 
 be subject to interference\, where the treatment of one individual can aff
 ect the outcome of another (e.g.\, vaccines and herd immunity). Many appro
 aches to addressing interference have arisen in the last decade and most b
 egin by modeling the interference as a network. However\, the majority of 
 these approaches require full knowledge of the underlying network\, which 
 is a problem in many practical settings where such information is unavaila
 ble. Thus\, to address this critical gap\, Cortez-Rodriguez presents an ap
 proach that achieves unbiased estimates of causal effects without requirin
 g fine-grain knowledge of the underlying interference network. Then\, she 
 presents a way to incorporate two different types of network information t
 o improve performance. Cortez-Rodriguez concludes with some promising dire
 ctions for future work.
CATEGORIES:Faculty,Staff,Students
LOCATION:Shanahan Center\, 320 E. Foothill Blvd.\, Claremont\, CA\, 91711\,
  United States
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 nter:geo:0,0
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DTSTART:20251102T010000
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