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Excited to be speaking on causal derivative effects tomorrow with a host of other exciting talks on the agenda. Don't miss it! #eurocim2021 Full programme: bit.ly/3ozQQva

Super talks this afternoon on recent research in #causalinference with #influencefunctions by Ollie Hines Ivana Malenica nima hejazi Daniel Scharfstein EuroCIM hosted by @LSHTMstatmethod

Nice to see my paper with Stijn Vansteelandt Karla DO on mediation analysis published. tldr: Non-parametric inference is nice, but here a semi-parametric model means we can avoid density specification and still be robust rdcu.be/ckYEi

Weds 2nd June at 4pm UK time, join us for the full version of EuroCIM talk "Parameterising and inferring the effect of a continuous exposure using average derivative effects" by Ollie Hines (abstract in picture). #causalinference Zoom link lshtm.zoom.us/j/96892759802

Modern statistical methods often use estimators based on efficient influence functions. See our latest tutorial paper on the subject with Oliver Dukes, Karla DO Stijn Vansteelandt arxiv.org/abs/2107.00681

Very much looking forward to learning different perspectives on this work, and so grateful to Mark van der Laan who inspired us to think more about assumption-lean inference, and to the RSS research section for selecting this paper for discussion.

🚨🚨New preprint with Karla DO Stijn Vansteelandt 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 arxiv.org/abs/2109.13124

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Thankful for the Central Limit Theorem

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 Karla DO Stijn Vansteelandt doi.org/10.1080/000313…

Check out our latest preprint where we propose new CATE variable importance measures for understanding heterogeneous causal effects. Pretty excited about this one! Karla DO Stijn Vansteelandt: arxiv.org/abs/2204.06030

Just updated the section on influence function derivation in my book. Closely follows the great tutorials by Edward Kennedy and Ollie Hines! Hope it helps folks who aren't as mathematically fluent. alejandroschuler.github.io/mci/0cb2ffe5e5…

Riesznet (Victor Chernozhukov #peace 🇺🇦 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 arxiv.org/abs/2308.05456 Karla DO Stijn Vansteelandt

New preprint is out!! We construct average moment estimators using constrained neural nets with some nice links to Auto-debiasing/TMLE. arxiv.org/abs/2409.19777 Really enjoyed working on this one with my supremely talented younger brother