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X-WR-CALNAME:Causal inference and machine learning in mobile health: Modeli
ng time-varying effects using longitudinal functional data
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UID:2024-10-21-tianchen-qian@cml.ics.uci.edu
DTSTAMP:20241021T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20241021T130000
DTEND;TZID=America/Los_Angeles:20241021T140000
SUMMARY:[CML Seminar] Tianchen Qian: Causal inference and machine learning
in mobile health: Modeling time-varying effects using longitudinal functio
nal data
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Tianchen Qian\, Assistant Professor of Statistics\, University
of California\, Irvine\n\nTitle: Causal inference and machine learning in
mobile health: Modeling time-varying effects using longitudinal functional
data\n\nAbstract: To optimize mobile health interventions and advance dom
ain knowledge on intervention design\, it is critical to understand how th
e intervention effect varies over time and with contextual information. Th
is study aims to assess how a push notification suggesting physical activi
ty influences individuals' step counts using data from the HeartSteps micr
o-randomized trial (MRT). We propose the first semiparametric causal excur
sion effect model with varying coefficients to model the time-varying effe
cts within a decision point and across decision points in an MRT. We propo
se a two-stage causal effect estimator that uses machine learning and is r
obust against a misspecified high-dimensional outcome regression nuisance
model. Our analysis provides new insights into individuals' change in resp
onse profiles due to the activity suggestions.\n\nhttps://cml.ics.uci.edu/
seminars/2024-10-21-tianchen-qian
X-ALT-DESC;FMTTYPE=text/html:Tianchen Qian\, Assistant P
rofessor of Statistics\, University of California\, Irvine
Title
: Causal inference and machine learning in mobile health: Modeling tim
e-varying effects using longitudinal functional data
Abstract: To optimize mobile health interventions and advance domain knowledge on
intervention design\, it is critical to understand how the intervention e
ffect varies over time and with contextual information. This study aims to
assess how a push notification suggesting physical activity influences in
dividuals' step counts using data from the HeartSteps micro-randomized tri
al (MRT). We propose the first semiparametric causal excursion effect mode
l with varying coefficients to model the time-varying effects within a dec
ision point and across decision points in an MRT. We propose a two-stage c
ausal effect estimator that uses machine learning and is robust against a
misspecified high-dimensional outcome regression nuisance model. Our analy
sis provides new insights into individuals' change in response profiles du
e to the activity suggestions.
https://cml.ics.uci.edu/seminars/2024-10-2
1-tianchen-qian
URL:https://cml.ics.uci.edu/seminars/2024-10-21-tianchen-qian
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