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X-WR-CALNAME:Towards Causal Revolution: On Learning Heterogeneity and Non-S
puriousness in Causal Graphs
X-WR-TIMEZONE:America/Los_Angeles
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DTSTART:20070311T020000
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UID:2023-05-01-hengrui-cai@cml.ics.uci.edu
DTSTAMP:20230501T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20230501T130000
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SUMMARY:[CML Seminar] Hengrui Cai: Towards Causal Revolution: On Learning H
eterogeneity and Non-Spuriousness in Causal Graphs
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Hengrui Cai\, Assistant Professor of Statistics\, University of
California\, Irvine\n\nTitle: Towards Causal Revolution: On Learning Hete
rogeneity and Non-Spuriousness in Causal Graphs\n\nAbstract: The causal re
volution has spurred interest in understanding complex relationships in va
rious fields. Under a general causal graph\, the exposure may have a direc
t effect on the outcome and also an indirect effect regulated by a set of
mediators. In this talk\, we introduce a new statistical framework to comp
rehensively characterize causal effects with multiple mediators\, namely A
Nalysis Of Causal Effects (ANOCE). Built upon such causal impact learning\
, we focus on two emerging challenges: heterogeneity and spuriousness. We
conceptualize heterogeneous causal graphs (HCGs) and propose to learn a cl
ass of necessary and sufficient causal graphs (NSCG) that only contain cau
sally relevant variables by utilizing the probabilities of causation. Acro
ss empirical studies\, we show that the proposed algorithms outperform exi
sting ones and can reveal true heterogeneous and non-spurious causal graph
s.\n\nhttps://cml.ics.uci.edu/seminars/2023-05-01-hengrui-cai
X-ALT-DESC;FMTTYPE=text/html:Hengrui Cai\, Assistant Pro
fessor of Statistics\, University of California\, Irvine
Title:<
/b> Towards Causal Revolution: On Learning Heterogeneity and Non-Spuriousn
ess in Causal Graphs
Abstract: The causal revolution has spu
rred interest in understanding complex relationships in various fields. Un
der a general causal graph\, the exposure may have a direct effect on the
outcome and also an indirect effect regulated by a set of mediators. In th
is talk\, we introduce a new statistical framework to comprehensively char
acterize causal effects with multiple mediators\, namely ANalysis Of Causa
l Effects (ANOCE). Built upon such causal impact learning\, we focus on tw
o emerging challenges: heterogeneity and spuriousness. We conceptualize he
terogeneous causal graphs (HCGs) and propose to learn a class of necessary
and sufficient causal graphs (NSCG) that only contain causally relevant v
ariables by utilizing the probabilities of causation. Across empirical stu
dies\, we show that the proposed algorithms outperform existing ones and c
an reveal true heterogeneous and non-spurious causal graphs.
https://cml.ic
s.uci.edu/seminars/2023-05-01-hengrui-cai
URL:https://cml.ics.uci.edu/seminars/2023-05-01-hengrui-cai
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