New preprint is out!! We construct average moment estimators using constrained neural nets with some nice links to Auto-debiasing/TMLE.
https://t.co/5dAgLD4qkc
Really enjoyed working on this one with my supremely talented younger brother
@UnibusPluram@ildiazm@mark_vdlaan Very cool! Did you know that it also represents a weighted average derivative effect too? See example 5 from https://t.co/aetkD8KDHH
Riesznet (@VC31415 et al.) has propelled Riesz representers into the causal inference limelight, but can we say more about the representers for similar looking estimands?
Answer: Yes
https://t.co/EjDPv0jTtG
@karlado@SVansteelandt
@wortmanncallejo @infornomics@DAcemogluMIT I just gave the repo a bit of an update and the tests seem to be passing now. Do you mind raising an issue if something is still broken? https://t.co/vi9ICfVcdK
Should you use projection estimands or derivative effects for continuous exposures?
It turns out that they are the same thing* (along with ATEs) + you can use the R-learner for estimation.
New preprint with @karlado@SVansteelandt
https://t.co/EjDPv0jTtG
@UnibusPluram@AngYu_soci@edwardhkennedy Iโm honoured to be mentioned in the same sentence as Edward and Alejandro! Not much to add except that the EIF derivation youโre after is a special case (X=Y) of Example 7 in our paper: https://t.co/oOPmuR5qHH
Just updated the section on influence function derivation in my book. Closely follows the great tutorials by @edwardhkennedy and @hines8! Hope it helps folks who aren't as mathematically fluent.
https://t.co/G1El858n4y
Check out our latest preprint where we propose new CATE variable importance measures for understanding heterogeneous causal effects. Pretty excited about this one! @karlado@SVansteelandt: https://t.co/6qMR5QjAEv
@d_s_rod @karlado@SVansteelandt I agree! We gave a workshop on this material at the most recent @TheEuroCIM meeting but that was some time ago now. Iโll tweet about it if/when we plan another
Our tutorial paper has just been published! We shed some light on the dark art of influence curve derivation, which is at the core of machine learning estimators in causal inference. Oliver Dukes @karlado@SVansteelandt
https://t.co/TkMwSo1nNP
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๐จ๐จNew preprint with @karlado@SVansteelandt on average derivative effects in causal inference.
TLDR - we think ADEs are a natural generalization of the average treatment effect to continuous exposures with some nice connections https://t.co/S22VLbL4ff
For anyone attending the virtual #JSM2021 - catch my talk today on new assumption-lean mediation estimands, developped with @SVansteelandt
https://t.co/W4E12JfT4i