Jennifer Hill
Professor of Applied Statistics and Data Science
Develops Bayesian nonparametric methods for causal inference, such as BART-based estimation, for policy research.
Jennifer Hill's lab at NYU works on probabilistic & causal inference, learning theory & optimization and efficient training & inference. 7 papers since 2023. Applications close .
At a glance
- papers, 2023–2026
- 2/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 0 · collecting
- 1 papers2025 · T1 0
- 2 papers2024 · T1 0
- 1 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference4 papers
- Efficient training & inference2 papers
- Learning theory & optimization1 paper
- develops bayesian0 papers
- nonparametric0 papers
Paper list
From the lab homepage.
20263 papers
- AJE Advances: Research in EpidemiologyCommunicating Research Findings: How Linking Terms and Context Affect the Interpretation of Conclusions
- The American StatisticianScaffolding responsible software use: evaluating the effectiveness of a causal inference tool
- DemographyThe Effect of the Great Recession on U.S. Fertility: Causal Estimates from a Novel Cohort Discontinuity Design
20251 paper
- Journal of Occupational and Environmental MedicineWho Works Non-Day Shifts? An Investigation of Population and Within-Cohort Trends
20242 papers
- Statistics Education Research JournalCausal Language and Statistics Instruction: A randomized experiment
- PlosONEComparing online versus laboratory measures of speech perception in older children and adolescents
20231 paper
- Handbook of Multivariate Matching and Weighting for Causal InferenceMachine Learning for Causal Inference
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- SGNTU
Yingzhen Li
- Probabilistic & causal inference
- GBUCL
Andrew Saxe
- Computational neuroscience & cognition
- SGNUS
Bryan Kian Hsiang Low
- Probabilistic & causal inference
- GBUCL
Arthur Gretton
- Learning theory & optimization
- CNTsinghua
Hongning Wang
- Reinforcement learning
- USUCSD
Yian Ma
- Reinforcement learning
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
NYUcloses