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Alec McClean @alecmcclean.bsky.social · 15d

Herb Susmann @herbps10.bsky.social · 15d

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

Non-overlap Average Treatment Effect Bounds by Herbert P. Susmann, Alec McClean, and Iván Díaz

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Alec McClean @alecmcclean.bsky.social · Jul 16

Although estimator is complex, some nice properties arise from the construction: in particular, we can examine distribution of cumulative weights across subjects, like in single-timepoint weighting

Distribution of cumulative weights, with one for each subject.

Alec McClean @alecmcclean.bsky.social · Jul 16

Cross-world"ness" --> nuances in identification and estimation

- ID: Need strong seq. rand., but still possible w/out positivity
- Est: new EIF for doubly robust estimator involves additional term w/ covariate density ratio across the target regimes

Covariate density ratio across two target regimes

Second half of EIF, w/ additional novel term involving covariate density ratio across regimes

ID. No positivity needed. Just need weights to behave well, which is possible by construction (eg, overlap, trimming)

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Alec McClean @alecmcclean.bsky.social · Jul 16

These fx are

- "Cross-world"
- "Mechanism-relevant" (they target mean diff in POs we care about)
- **Not** "policy-relevant" (they're not implementable)

This tradeoff arises elsewhere (mediation, censoring by death). Ours is another example:

What you want to know != what you can implement

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Alec McClean @alecmcclean.bsky.social · Jul 16

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

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Alec McClean @alecmcclean.bsky.social · Jun 12

We show that contrasts in flip effects yield WATEs when t=1 and non-baseline weighting for t>1

We also give some new doubly robust estimation results:
1. typical multiply robust estimator is twice as robust as people had thought
2. new sequentially doubly robust style estimator

Alec McClean @alecmcclean.bsky.social · Jun 12

Flip ints are built from a target tx and a weight (eg overlap wt, trimming indicator):

1. If subject would take target tx, do nothing
2. O/w flip subject to target with prob equal to the weight

Allows you to target any regime (eg, always treated) while adjusting to pos violations as needed

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Alec McClean @alecmcclean.bsky.social · Jun 12

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 · Jun 12

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

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Alec McClean @alecmcclean.bsky.social · May 13

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 · Apr 8

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...

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Alec McClean @alecmcclean.bsky.social · Apr 8

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...

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Reposted by Alec McClean

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · Jan 15

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

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Reposted by Alec McClean

Wenbo Wu @wenbowu.bsky.social · Jan 15

📢📢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!

ASA Community

The ASA Community is an online gateway for member collaboration and connection.

community.amstat.org

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Reposted by Alec McClean

Rachel Leah Childers @donskerclass.bsky.social · Jan 14

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....

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Reposted by Alec McClean

arxiv.stat.ME @arxiv-stat-me.bsky.social · Jan 9

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

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Alec McClean @alecmcclean.bsky.social · Jan 2

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?

Reposted by Alec McClean

Rachel Leah Childers @donskerclass.bsky.social · Dec 31

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

Papers I Liked 2024 | David Childers

This has been another year where I felt like I slacked on my reading, and that probably is genuinely true for the tumultuous last half, but my read folder lists 154, so I can pick out a few that I lik...

donskerclass.github.io

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Alec McClean @alecmcclean.bsky.social · Dec 30

@idiaz.bsky.social et al. 2020

arxiv.org/pdf/2006.01366

Generalizes to a large class of ints. Also gives great review of other innovations from 2010s

Bonus: for identification, it uses an NPSEM -- an alternative to SWIGs. NPSEMs come from do-why lit; great for discussing asmps w/ practitioners

arxiv.org

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Alec McClean @alecmcclean.bsky.social · Dec 30

2) Young et al. 2014 pmc.ncbi.nlm.nih.gov/articles/PMC...

Ints that depend on natural value of trtment. Very easy-to-read! Appendix B is great on ID.

Further reading: the SWIG papers; primer first (stats.ox.ac.uk/~evans/uai13/Richardson.pdf), and original (R&R '13) when you're feeling brave!

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

pmc.ncbi.nlm.nih.gov

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Alec McClean @alecmcclean.bsky.social · Dec 30

1) Robins et al. 2004 www.jstor.org/stable/pdf/r...

Addresses or foreshadows lots of subsequent work on time-varying data. The data analysis in Section 6 helped me build intuition for earlier parts of the paper.

www.jstor.org

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Alec McClean @alecmcclean.bsky.social · Dec 30

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

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Reposted by Alec McClean

Peter Hull @instrumenthull.bsky.social · Dec 27

What's the best paper you read this year?

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Alec McClean @alecmcclean.bsky.social · Dec 28

Not a 2024 paper, but new to me in 2024: www.sciencedirect.com/science/arti...

Cool results about estimation with extreme prop scores; limiting dists and inference even w/out CLT. Nicely resolved some q's I was thinking abt, before I spent too much time thinking about them (the perfect situation!)

Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores

This paper provides a framework for conducting valid inference for causal parameters without imposing strong variance or support restrictions on the p…

www.sciencedirect.com

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Alec McClean @alecmcclean.bsky.social · Dec 28

We analyze the effect of mothers’ smoking on infant birthweight, and see that accounting for uncertainty in estimating M alters CIs for ATE.

This was fun work with Edward and Zach Branson (sites.google.com/site/zjbrans...) and was a great project to finish my PhD!

9/9

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Alec McClean @alecmcclean.bsky.social · Dec 28

We incorporate M into our “calibrated” sensitivity models. Generically:

U <= GM

where G is sensitivity parameter.

We outline many choices for U and M and develop three specific models. Then identify bounds on ATE and give estimators that account for uncertainty in estimating M.

8/9

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Alec McClean @alecmcclean.bsky.social · Dec 28

Often calibration is somewhat informal w/out accounting for uncertainty in M. However, statistical error in estimating M is first order and can alter confidence intervals! Indeed, if calibration is goal, more intuitive to put M directly in model.

We explore ramifications of this reframing!

7/9

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