Vasilis Syrgkanis
Assistant Professor of Management Science and Engineering
Studies causal machine learning and econometrics, including double machine learning.
Vasilis Syrgkanis's lab at Stanford works on probabilistic & causal inference. 29 papers since 2023, 8 at top venues, mostly NeurIPS and COLT. 5 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 28%
- PhD students
- 5
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 12 papers2026 · T1 1 · collecting
- 9 papers2025 · T1 7
- 6 papers2024 · T1 0
- 2 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS, COLT, ICML, Operations Research, TMLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference10 papers
- causal8 papers
- econometrics0 papers
- studies causal0 papers
- management0 papers
Paper list
From the lab homepage.
202612 papers
- BiometrikaAutomatic Debiased Machine Learning for Covariate Shifts (opens in a new tab)
- Quantitative EconomicsConditional Influence Functions (opens in a new tab)
- NeurIPS26Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection (opens in a new tab)
- AISTATS26Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences (opens in a new tab)
- COLM2026Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning (opens in a new tab)
- TMLR (T1)Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration (opens in a new tab)
- ICML26Learning Treatment Representations for Downstream Instrumental Variable Regression (opens in a new tab)
- ReSTATLong Story Short: Omitted Variable Bias in Causal Machine Learning (opens in a new tab)
- AISTATS26Policy Learning with Abstention (opens in a new tab)
- ICML26Prescriptive Scaling Reveals the Evolution of Language Model Capabilities (opens in a new tab)
- EC26Switchback Price Experiments with Forward-Looking Demand (opens in a new tab)
- EC26The Double Diagonal Estimator: Reducing Bias in Two-Sided Marketplace Experiments (opens in a new tab)
20259 papers
- ICML (T1)A Meta-learner for Heterogeneous Effects in Difference-in-Differences (opens in a new tab)
- JASAAdversarial Estimation of Riesz Representers (opens in a new tab)
- Pacific Symposium on BiocomputingDetecting clinician implicit biases in diagnoses using proximal causal inference (opens in a new tab)
- NeurIPS (T1)Estimation of Treatment Effects in Extreme and Unobserved Data (opens in a new tab)
- NeurIPS (T1)It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation (opens in a new tab)
- COLT (T1)Orthogonal Causal Calibration (opens in a new tab)
- Operations Research (T1)Post Reinforcement Learning Inference (opens in a new tab)
- NeurIPS (T1)Preference Learning with Response Time (opens in a new tab)
- COLT (T1)Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation (opens in a new tab)
20246 papers
- ICLR24Adaptive Instrument Design for Indirect Experiments (opens in a new tab)
- AISTATS24Causal Q-Aggregation for CATE Model Selection (opens in a new tab)
- NeurIPS24Consistency of Neural Causal Partial Identification (opens in a new tab)
- ICLR24Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation (opens in a new tab)
- NeurIPS24Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity (opens in a new tab)
- NeurIPS24Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
Stanford Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- See program page
Confirmed on the program page.
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Data
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