David Alexander Bruns-Smith
Assistant Professor
Develops machine learning methods for causal inference, including debiased ML for instrumental variables and reinforcement learning, applied to macroeconomics and household finance.
David Alexander Bruns-Smith's lab at MIT works on reinforcement learning, probabilistic & causal inference and efficient training & inference. 4 papers since 2023, 2 at top venues, mostly AISTATS and FAccT. Applications close .
At a glance
- papers, 2023–2026
- 1/ year
- at T1 venues
- 50%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 2 papers2025 · T1 1
- 0 papers2024 · T1 0
- 1 papers2023 · T1 0
T1 venuesOtherVenues: AISTATS, FAccT, NeurIPS
Research focuspaper titles that use each topic's words
- Reinforcement learning2 papers
- Probabilistic & causal inference1 paper
- Efficient training & inference1 paper
- finance0 papers
- applied0 papers
Paper list
From the lab homepage.
20261 paper
20252 papers
- Journal of the Royal Statistical Society Series B: Statistical MethodologyAugmented Balancing Weights as Linear Regression (opens in a new tab)
- NeurIPS (T1)Ridge Boosting is Both Robust and Efficient (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
Similar labsnearest by research summary
- USUC Berkeley
Sanjit A. Seshia
- Compilers & programming languages
- GBCambridge
Zoubin Ghahramani
- Probabilistic & causal inference
- CNTsinghua
Chao Shang
- Reinforcement learning
- USCMU
Nicholas Boffi
- Generative & foundation models
- USNYU
Rumi Chunara
- Trustworthy & safe ML
- GBUCL
Peter Orbanz
- Learning theory & optimization
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.
MITcloses