Discover and read the best of Twitter Threads about #IPweighting
Most recents (1)
#Causalinference that talks the talk and walks the walk.
Claim: "Continuing #breastcancer screening past age 75 doesn't reduce 8-year breast cancer mortality."
Emulation of a #TargetTrial led by @xabieradrian with Medicare data
doi.org/10.7326/M18-11…
Let the discussion start.
Claim: "Continuing #breastcancer screening past age 75 doesn't reduce 8-year breast cancer mortality."
Emulation of a #TargetTrial led by @xabieradrian with Medicare data
doi.org/10.7326/M18-11…
Let the discussion start.
@xabieradrian @AnnalsofIM @HarvardEpi @harvard_data @HarvardBiostats @HarvardChanSPH @CMSGov @CMSgovPress @MonganInstitute @MassGeneralNews 2)
Because there's so much talk about #causalinference around here.
Computer scientists, economists, statisticians... talk a lot about the merits of #DeepLearning, instrumental variables, or whatever their preferred methodology is.
Everybody: This is your chance to shine.
Because there's so much talk about #causalinference around here.
Computer scientists, economists, statisticians... talk a lot about the merits of #DeepLearning, instrumental variables, or whatever their preferred methodology is.
Everybody: This is your chance to shine.
3)
No more toy examples. A real world question:
"At what age should #breastcancer screening stop?"
Need to compare the mortality of women under two dynamic screening strategies using a database of insurance claims with time-varying treatments and confounders.
How'd you do it?
No more toy examples. A real world question:
"At what age should #breastcancer screening stop?"
Need to compare the mortality of women under two dynamic screening strategies using a database of insurance claims with time-varying treatments and confounders.
How'd you do it?
