Ollie Hines

Data Scientist - PhD Biostats Grad of @LSHTMstatmethod - Former rower @imperialboat

🦋 - @ohines.bsky.social

[Ollie Hines @hines8]

@UnibusPluram @BerkeleyCTML Research statement optional. DEI statement mandatory??

[Ollie Hines @hines8]

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

[Ollie Hines @hines8]

@shortstein Not surprised it is hard - reminds me of this fun Riemann zeta function fact

[Ollie Hines @hines8]

@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

[Ollie Hines @hines8]

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

[Ollie Hines @hines8]

@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

[Ollie Hines @hines8]

@wortmanncallejo @infornomics @DAcemogluMIT Nice to hear this code is getting some use!

[Ollie Hines @hines8]

Plus @edwardhkennedy called them his new favorite estimands at #ACIC2023 🙌
Or the numerator of \Psi at least

[Ollie Hines @hines8]

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

[Ollie Hines @hines8]

@UnibusPluram @AngYu_soci @edwardhkennedy I’m honoured to be mentioned in the same sentence as Edward and Alejandro! Not much to add except that the EIF derivation you’re after is a special case (X=Y) of Example 7 in our paper: https://t.co/oOPmuR5qHH

[Alejandro Schuler @UnibusPluram]

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

[Ollie Hines @hines8]

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

[Ollie Hines @hines8]

@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. I’ll tweet about it if/when we plan another

[Ollie Hines @hines8]

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

[Peter Hull @instrumenthull]

Thankful for the Central Limit Theorem

[London School of Hygiene & Tropical Medicine @LSHTM]

Proud to be ranked 2nd in the world for Public, Environmental & Occupational Health & 4th for Infectious Diseases by @usnews 🎉

Based on research & reputation, this reflects the hard work of our amazing students & staff 🙌

Study with us 👉 https://t.co/pyM2l8wKxx #GlobalHealth

[Ollie Hines @hines8]

🚨🚨New 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

[Ollie Hines @hines8]

@cspan IMOoiooo i

[Ollie Hines @hines8]

@SVansteelandt Developed!

[Ollie Hines @hines8]

For anyone attending the virtual #JSM2021 - catch my talk today on new assumption-lean mediation estimands, developped with @SVansteelandt
https://t.co/W4E12JfT4i