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SUMMARY:Causal Inference and Decompositions for Sequence Data Using Genera
 tive Models - Susan Athey (Stanford University)
DTSTART:20260127T110000Z
DTEND:20260127T120000Z
UID:TALK241558@talks.cam.ac.uk
DESCRIPTION:This talk will review recent work adapting tools from causal i
 nference\, including tools for estimating decompositions\, average treatme
 nt effects\, and heterogeneous treatment effects\, to problems involving s
 equence data\, such as sequences of words in text\, sequences of jobs in w
 orker careers\, and sequences of measured behaviors and actions in custome
 r journeys.&nbsp\; We provide new theory tailored to these problems and ap
 ply the methods to the problem of estimating the gender wage gap in worker
  careers as well as decomposing the sources of changes in gender wage gaps
  over time.&nbsp\; We illustrate approaches to derive insight about causal
  effects\, including approaches to answer causal questions about how indiv
 idual trajectories evolve over time.
LOCATION:Seminar Room 1\, Newton Institute
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