New paper with @herbps10.bsky.social!

Herb Susmann@herbps10.bsky.social · 10mo ago

New preprint out on a way to handle structural and practical violations of the overlap (also known as positivity) assumption in causal inference -- as long as the outcome is bounded, we derive simple partial identification bounds on the ATE. With @alecmcclean.bsky.social and @idiaz.bsky.social

New paper 📜 We construct longitudinal effects tailored to isolated mean diff in two POs while adapting to positivity violations under both regimes. Some notes vv

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · last yr.

link 📈🤖 Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations (McClean, D\'iaz) When examining a contrast between two interventions, longitudinal causal inference studies frequently encounter positivity violations when one or both regimes are im

New paper! Weighting is great for addressing positivity violations, but it's unclear how to do it in longitudinal data. We propose a solution: "flip" interventions. These allow for weighing on non-baseline covariates and give effects robust to arbitrary positivity violations. Highlights below vv

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · last yr.

link 📈🤖 Longitudinal weighted and trimmed treatment effects with flip interventions (McClean, Levis, Williams et al) Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily g

Excited to present this again at ACIC (Th 1:15pm)! We realized trimming is a special version of weighting —> we generalized the analysis to longitudinal weighted effects “Longitudinal weighted and trimmed treatment effects with flip interventions” Draft: alecmcclean.github.io/files/long-w...

Alec McClean@alecmcclean.bsky.social · last yr.

Excited to present on Thursday @eurocim.bsky.social on new work with @idiaz.bsky.social on (smooth) trimming with longitudinal data! "Longitudinal trimming and smooth trimming with flip and S-flip interventions" Prelim draft: alecmcclean.github.io/files/LSTTEs...

Excited to present on Thursday @eurocim.bsky.social on new work with @idiaz.bsky.social on (smooth) trimming with longitudinal data! "Longitudinal trimming and smooth trimming with flip and S-flip interventions" Prelim draft: alecmcclean.github.io/files/LSTTEs...

link 📈🤖 Bridging Root-$n$ and Non-standard Asymptotics: Dimension-agnostic Adaptive Inference in M-Estimation (Takatsu, Kuchibhotla) This manuscript studies a general approach to construct confidence sets for the solution of population-level optimization, commonly referred to as M-estimation. Sta

📢📢The 4th Lifetime Data Science Conference will take place May 28–30, 2025, at New York Marriott at the Brooklyn Bridge in Brooklyn, NY, USA. This event will feature keynotes by Drs. Nicholas Jewell and Mei-Ling Lee, short courses, 60+ invited sessions, and a banquet on May 29. Register and join us!

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For the Spring semester, I am restarting my free weekly open office-hours for anyone in the world with Econometrics questions. Wednesdays 10-12AM Eastern or by appointment; sign up and drop by! Details and sign up at donskerclass.github.io/OfficeHours....

Rebecca Farina, Arun Kumar Kuchibhotla, Eric J. Tchetgen Tchetgen Doubly Robust and Efficient Calibration of Prediction Sets for Censored Time-to-Event Outcomes https://arxiv.org/abs/2501.04615

For IV folks: what's a good resource on time-varying 2SLS? Data = time-varying {covariates, instruments, outcomes} Asmp: a version of longitudinal 2SLS; ie linear SEM in 1st & 2nd stages, over time Time-varying data seems to introduce some nuance. Is there a textbook treatment of this?

My traditional end-of-year review: some papers I read and liked in 2024. donskerclass.github.io/post/papers-...

Related to lit review in an ongoing project: for complex time-varying ints and identification in epi/bio, I think these three papers are great starting points: www.jstor.org/stable/pdf/r... pmc.ncbi.nlm.nih.gov/articles/PMC... arxiv.org/pdf/2006.01366 Details below. What are other's favorites?

Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data

pmc.ncbi.nlm.nih.gov

What's the best paper you read this year?

My 2024 “highlights” (or what consumed my work year): 1. Double cross-fitting (arxiv.org/abs/2403.15175) 2. Calibrated sensitivity models (arxiv.org/abs/2405.08738) 3. Fair comparisons (arxiv.org/abs/2410.13522) For #3, bsky.app/profile/alec.... Below: gory details for 1 and 2 (new to bsky) 1/9

Alec McClean@alecmcclean.bsky.social · 2y ago

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.

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

@wenbowu.bsky.social and I are looking for a postdoc! please reach out if you are interested.