Edward H. Kennedy
@edwardhkennedy.bsky.social
assoc prof of statistics & data science at Carnegie Mellon https://www.ehkennedy.com/ interested in causality, machine learning, nonparametrics, public policy, etc
Went to look up textbook results after getting the nagging feeling that an ML paper was reinventing classical ideas, and found this gem: "Not reading to the end of Le Cam's papers became not uncommon in later years. His ideas have been regularly rediscovered." At least they're in good company.
From twitter: A short thread: It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust parameter/functional estimation / TMLE / double/debiased/orthogonal ML etc etc) were first proposed many decades ago. I think this is widely under-appreciated!
"Randomized trials should be used to answer any causal question that can be so studied... But the reality is that observational methods are used everyday to answer pressing causal questions that cannot be studied in randomized trials." - Jamie Robins, 2002 tinyurl.com/4yuxfxes tinyurl.com/zncp39mr
Thank you Alec for leading this project, I learned a lot! This paper has a very useful study of what contrasts are feasible in situations with many treatments and positivity violations, including necessary assumptions and efficient one-step estimators. Check it out!
Alec McClean@alecmcclean.bsky.social · 2y agoNew-ish paper alert! arxiv.org/abs/2410.13522 We tackle the challenge of comparing multiple treatments when some subjects have zero prob. of receiving certain treatments. Eg, provider profiling: comparing hospitals (the “treatments”) for patient outcomes. Positivity violations are everywhere.
New-ish paper alert! arxiv.org/abs/2410.13522 We tackle the challenge of comparing multiple treatments when some subjects have zero prob. of receiving certain treatments. Eg, provider profiling: comparing hospitals (the “treatments”) for patient outcomes. Positivity violations are everywhere.
Fair comparisons of causal parameters with many treatments and positivity violations
Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each targeting a different ...
arxiv.org
Found slides by Ankur Moitra (presented at a TCS For All event) on "How to do theoretical research." Full of great advice! My favourite: "Find the easiest problem you can't solve. The more embarrassing, the better!" Slides: drive.google.com/file/d/15VaT... TCS For all: sigact.org/tcsforall/
@bonv.bsky.social presented this at NYU this week -- terrific work with an excellent presentation (no surprise there)! I found the connections to higher-order estimators and the orthogonalizing property of the U-stat kernel fascinating&illuminating.
Should we use structure-agnostic (arxiv.org/abs/2305.04116) or smooth (arxiv.org/pdf/1512.02174) models for causal inference? Why not both? Here we propose novel hybrid smooth+agnostic model, give minimax rates, & new optimal methods arxiv.org/pdf/2405.08525 -> fast rates under weaker conditions
I see renewed discussion on #statsky about the interpretation of confidence intervals. I will leave here this quote from Larry Wasserman's All of Statistics, which I love. Controlling one's lifetime proportion of studies with an interval that does not contain the parameter is surely desirable!
Reminder/plug: my graduate-level monograph on "Topics and Techniques in Distribution Testing" (FnT Comm. and Inf Theory, 2022). 📖 ccanonne.github.io/survey-topic... [Latest draft+exercise solns, free] 📗 nowpublishers.com/article/Deta... [Official pub] 📝 github.com/ccanonne/sur... [LaTeX source]
"There’s no way you can just sit down & do a `big thing', or at least I can’t. So I just went back to doing lots of little things, & hoping that some of them will turn out okay. Statistics is a wonderfully forgiving field... all you have to do is get an idea & keep at it." - Brad Efron #statsquotes
Kandiros, Pipis, Daskalakis, and Harshaw have a really Interesting new arxiv preprint on "conflict graph designs" for interference/spillovers: arxiv.org/abs/2411.10908 For GATE estimation the improvement is very significant and I'm optimistic/excited about how the ideas will impact the literature..!
The European Causal Inference Meeting 2025 is coming to Ghent! ✨ Share your work with experts across the globe – abstract submission for oral & poster presentations is now open! eurocim.org/abstracts.html
New paper! arxiv.org/pdf/2411.14285 Led by amazing postdoc Alex Levis: www.awlevis.com/about/ We show causal effects of new "soft" interventions are less sensitive to unmeasured confounding & study which effects are *least* sensitive to confounding -> makes new connections to optimal transport
As my first post on this platform, allow me to advertise the RL theory lecture notes I have been developing with Sasha Rakhlin: arxiv.org/abs/2312.16730 (shameless repost of my pinned tweet)
What’s the best way to structure a quantitative methods sequence? Our current take is roughly: 1) Probability/Inference/Regression 2) Causal Inference 3) Model based inference (MLE/Bayes) 4) Machine Learning
In this paper we consider incremental effects of continuous exposures: arxiv.org/abs/2409.11967 i.e., soft interventions on cts treatments like dose, duration, frequency it turns out exponential tilts preserve all nice properties of incremental effects with binary trt (arxiv.org/abs/1704.00211)
This work was led by Alex Levis - an amazingly talented postdoc, who I've been lucky to work with on a surprisingly wide variety of really interesting causal inference problems www.awlevis.com/about/
This paper was so fun: arxiv.org/pdf/2301.121... We give new methods for estimating bounds on avg treatment effects - trt is confounded, but an instrument is available. Super common in practice The bounds are non-smooth, so std efficiency theory isn't applicable Lots of useful nuggets throughout!
Very excited about this paper! arxiv.org/abs/2305.04116 We study if one can improve popular semiparametric / doubly robust / DML causal effect estimators - w/o adding structural assumptions... Short answer: nope! Turns out these methods are minimax optimal here www.ehkennedy.com/uploads/5/8/...
there are *surprisingly many* open problems when it comes to theory/methods in causal inference check out this talk by Siva Balakrishnan for an excellent & comprehensive summary of the state of the art www.youtube.com/live/Mnum0Ox... www.stat.cmu.edu/~siva/
Short story - the ideas behind “causal ML” and “double machine learning” go back at least 40 years Here is an estimator from a 1982 textbook that today would be called double machine learning or something similar
Edward H. Kennedy@edwardhkennedy.bsky.social · 2y agoFrom twitter: A short thread: It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust parameter/functional estimation / TMLE / double/debiased/orthogonal ML etc etc) were first proposed many decades ago. I think this is widely under-appreciated!
From twitter: A short thread: It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust parameter/functional estimation / TMLE / double/debiased/orthogonal ML etc etc) were first proposed many decades ago. I think this is widely under-appreciated!
