Arman Oganisian
@StableMarkets
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Statistician | Assistant professor at @BrownBiostats | Nonparametric Bayesian methods for causal inference. https://t.co/tdV1V2KpHg
Providence, RI
Joined March 2011
Excited to share that @PCORI has awarded funding for our proposed work developing Bayesian machine learning methods for causal inference!. Thanks to Roee Gutman (co-PI) & others at @Brown_SPH . @Brown_Epi @BrownUResearch @BrownMedicine @BrownUniversity.
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I was asked to give some advise to current students in this spotlight - they may not apply to everyone, but these worked well for me.
Our latest alumni spotlight features Arman Oganisian, PhD Biostatistics ’21, now Assistant Professor at Brown. His work on Bayesian causal inference builds on the mentorship and collaboration that shaped his experience at Penn. Read more:
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Streaming live now on YouTube:. Andrew Gelman is talking about @StatModeling is talking about "Taking Our Models Seriously".
StanBio Connect 2025: Advancing Biomedical Research with Stan. This event is streaming live tomorrow and I’ll be speaking about some work on Bayesian causal inference with survival outcomes.
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StanBio Connect 2025: Advancing Biomedical Research with Stan. This event is streaming live tomorrow and I’ll be speaking about some work on Bayesian causal inference with survival outcomes.
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I’m teaching a 3-hour session on Bayesian causal inference at Penn Causal Inference Summer Institute, 5/27-5/30. Virtual attendance options are available. There are sessions on many other really cool topics as well -check out the agenda:.
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RT @Brown_SPH: Congratulations to the students, staff & faculty honored Wednesday at the #BrownSPH 2025 Dean's Awards Ceremony!🏅. “You remi….
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New paper here w/ Tony Linero on Bayesian causal inference:. Independent priors on propensity score & outcome models often imply a strong prior on no *measured* confounding - a prior belief that 1) we rarely hold and 2) leads to bad frequentist performance.
I'm pleased to announce the publication of our special issue featuring an opinion piece by Aronow, Robins, Saarinen, Sävje: “Nonparametric Identification Is Not Enough, but Randomized Controlled Trials Are" along with 6 commentaries:
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Slides for this talk are posted on my site!.
I’ll be talking about this paper on Bayesian causal inference w/ recurrent events @ #ENAR2025. Come check out our session!. Session 50. It’s All Aabout the Estimand: Asking the Right Questions to Best Inform Health Policy Decisions. March 24: 1:45-3:30pm; at Oakley, floor 4.
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I’ll be talking about this paper on Bayesian causal inference w/ recurrent events @ #ENAR2025. Come check out our session!. Session 50. It’s All Aabout the Estimand: Asking the Right Questions to Best Inform Health Policy Decisions. March 24: 1:45-3:30pm; at Oakley, floor 4.
Check out our new paper developing Bayesian causal inference methods for recurrent event outcomes!. We handle several complexities such as censoring, terminal events, and treatment timing misalignment. Best part: the Bayesian models are all implementable in Stan (@mcmc_stan).
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Bayesian causal survival analysis has never been so easy! . Check out Han Ji’s (@BrownBiostats) paper & package, in press @ObservStudies. Convenient syntax, S3 classes & methods, help files, & efficient MCMC via @mcmc_stan.
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Software update: @Daniel_R_Kowal was kind enough to include an implementation of our hierarchical Bayesian bootstrap in his SeBR R package (which also has other great regression tools!) - complete w/ help files and examples.
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Check out this new paper by @BrownBiostats PhD Candidate Esteban Fernández-Morales. He develops innovative Bayesian spatial shrinkage methods for causal inference with spillovers and uses it to assess the effect of Philadelphia's 2017 beverage tax.
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RT @biostatsfun: @StableMarkets @mcmc_stan @BrownUniversity @Brown_SPH @BrownUResearch @BrownBiostats Wow! Ty! I have been using your causa….
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Check out our new paper developing Bayesian causal inference methods for recurrent event outcomes!. We handle several complexities such as censoring, terminal events, and treatment timing misalignment. Best part: the Bayesian models are all implementable in Stan (@mcmc_stan).
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Check out our paper developing Bayesian methods for causal inference with incomplete outcome and covariates (all doable in @mcmc_stan!):. Recording of a talk on this at NYU's biostatistics symposium can be found here (4:04:20):
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Happy to have such great colleagues at Brown!
🎉 Congratulations to @BrownBiostats on 30 years of innovation & collaboration!.“This milestone is . an opportunity to acknowledge the partnerships we’ve developed at @BrownUniversity and to express our deep gratitude.”.— Professor and Chair @jwhogan42.
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Looking forward to this symposium tomorrow! I’ll be talking about some Bayesian causal modeling for HIV.
Our Division is hosting its inaugural yearly Biostatistics Symposium, and this year the topic is Causal Inference! We have an exciting lineup of speakers listed below. If you are in the NYC area, please join us! Link to register in the QR below.
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Back from an incredible @BIRS_Math workshop at Casa Matemática Oaxaca. A weeklong dive into Bayes - ranging from nonparametrics to MCMC & causal inference Big thanks to Alejandra Avalos (@AleAviP), Peter Mueller, Fan Bu (@FannyBu214), & other organizers.
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Check out this recent paper!.
Observational studies are important in osteoporosis, 🦴but what to do when 2 studies with the *same* research question reach *different* conclusions? 🤔@StableMarkets, #DougPKiel and I discuss in the latest issue of @The_JBMR re: DMAB vs. ALN.
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