Skip to main content

About Us

Our lab develops machine learning and artificial intelligence methodologies for learning causal effects from complex observational and experimental datasets. Our mission is to automate causal inference and make it accessible to decision-makers across various domains. Our methods are motivated by and have been applied in fields such as sustainability, healthcare, operations management, and digital experimentation. The lab's principal investigator has led the development of open-source software widely used in the industry, and the lab continues to support and develop open-source tools that lower barriers to entry in causal machine learning for data scientists.

Supported By

NSF

Google

Amazon

Bodossaki Foundation

Schmidt Sciences

Intuit

Netflix

News

Recent research, releases, and milestones from the lab.

Applications

We partner across domains where careful evidence can improve consequential decisions.

Causal AI for Sustainability

Our lab works on understanding causal factors of vulnerability of a region to natural hazards, with the aim of informing appropriate policy interventions.

Causal AI for Healthcare

Our lab works on topics such as detecting implicit biases in medical decisions, understanding the causal effects of latent treatment dimensions from observational patient treamtent trajectories.

Causal AI for Operations

Our lab works on data-driven pricing, customer segmentation, return-on-investment and the impact of GenAI at work.

Causal AI for Digital Experiments

Our lab works on developing data analytic tools for experimentation in digital platforms, such as estimation of heterogeneous effects from large scale A/B tests and recommendation A/B tests.

Foundations

We develop foundational methods and theory for learning about cause and effect and using that knowledge to make better decisions across diverse settings.

Heterogeneous Effects: Estimation, Inference and Policy Learning

How does the effect of a treatment or intervention vary as a function of characteristics of the treated unit and how should we personalize a treatment policy? Our lab works on ML techniques for the estimation of heterogeneous effects and for policy learning.

Debiased Machine Learning

ML methods typically introduce regularization to balance bias and variance of a learned model. Construction of confidence intervals requires small bias compared to variance. We have developed many techniques for automatic debiasing of many downstream causal quantities of interest.
Source Condition Double Robust Inference on Functionals of Inverse Problems

Automatic Debiased Machine Learning for Dynamic Treatment Effects

Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression

Adversarial Estimation of Riesz Representers, JASA (Journal Version)

Inference on Strongly Identified Functionals of Weakly Identified Functions, COLT23 and JRSS-B (Journal Version)

Debiased Machine Learning without Sample-Splitting for Stable Estimators, NeurIPS22

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests, ICML22

Knowledge Distillation as Semi-Parametric Inference, ICLR21

Orthogonal Machine Learning: Power and Limitations, ICML18

Non-Parametric Instrumental Variables

Instrumental variables (IV) is a powerful technique for parsing causal effects from observational data with unobserved confounding, leveraging natural experimentation. Classical IV methods either make strong parametric assumptions, or are fully non-parametric and suffer from the curse of dimensionality (only applicable with a small number of variables). We have developed generic ML techniques for instrumental variable methods, allowing for flexible causal modeling when having access to instruments.
Regularized DeepIV with Model Selection

Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness, COLT23

Minimax Estimation of Conditional Moment Models, NeurIPS20

Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments, NeurIPS19

Adaptive Experiments

Adaptive experiments can lead to better decisions by appropriately changing the randomization probabilities based on past observations. Our lab works on novel adaptive experimentation algorithms that can handle high-dimensional settings. Adaptation can also lead to problems when it comes to retrospective analysis and inference. Our lab has introduced many techniques for the construction of confidence intervals for off-policy evaluation from adaptively collected data.
Inference on the Value of Policy Improvement in Adaptive Experiments

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity, NeurIPS24

Post Reinforcement Learning Inference, OR (R&R)

Adaptive Instrument Design for Indirect Experiments, ICLR24

Dynamically Aggregating Diverse Information, Econometrica, 2021 (Journal Version)

Semiparametric Contextual Bandits, ICML18

Accurate Inference for Adaptive Linear Models, ICML18

Low-rank Bandit Methods for High-dimensional Dynamic Pricing, NeurIPS19

Dynamic Treatment Regimes

In many settings, units receive multiple treatments over time in an adaptive and autocorrelated manner. Causal inference in such settings is substantially harder as we need to take into account the intertemporal effects of the treatments. The area typically goes by the name of dynamic treatment regime. Our lab has developed ML based estimation techniques for the evaluation and inference of complex dynamic policies from offline observational data.
Dynamic Local Average Treatment Effects

Inference on Optimal Dynamic Policies via Softmax Approximation

Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects

Automatic Debiased Machine Learning for Dynamic Treatment Effects

Estimating the Long-Term Effects of Novel Treatments, NeurIPS21

Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation, NeurIPS21

Neural Causal Models

Causal inference can be framed as a neural generative model problem. This lends to general purpose algorithms for identification and partial identification via stochastic gradient descent training. Our lab develops methods and provable guarantees for such neural-causal approaches to causal identification and estimation.
Consistency of Neural Causal Partial Identification, NeurIPS24

Partial Identification of Treatment Effects with Implicit Generative Models, NeurIPS22

Causal Representation Learning

When dealing with complex data modalities such as text and images, the raw data do not correspond to the high level causal variables on which we want to perform our causal analysis. Causal representation learning aims to uncover such causal latent factors in an automated manner. Our lab has proven identifiability guarantees and associated algorithms for causal latent factor discovery.
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity, NeurIPS24

Towards efficient representation identification in supervised learning, CLear22

Sensitivity Analysis

Causal inference is necessarily based on domain assumptions. Sensitivity analysis offers a way to quantify the impact on the causal quantity of the violation of these assumptions. Our lab has developed general purpose sensitivity bound construction procedures with rigorous statistical guarantees, even when ML tecnniques are used in the estimation process.
Long Story Short: Omitted Variable Bias in Causal Machine Learning, ReSTAT (Journal Version)

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions, ICML21 Workshop

Large Language Models

Large language models have become transformative tools across many applications. Our lab works on understanding the causal mechanisms underlying LLM capabilities and behaviors, developing methods for alignment via preference learning from expert demonstrations, and studying how LLM capabilities scale with model size and training data.
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities, ICML26

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning, COLM2026

Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences, AISTATS26

Preference Learning with Response Time, NeurIPS25

Personalized Adaptation via In-Context Preference Learning, Arxiv24

Minimax Lower Bounds for Causal Estimation

The causal inference literature has produced many estimation algorithms that each have their pros and cons. A guiding principle on whether there is room for improvement is minimax optimality frameworks: what is the best achievable estimation quality, when we assume that our data stem from a family of distributions. Our lab has developed such minimax optimal lower bounds that match existing upper bounds of popular algorithms in a structure agnostic minimax optimal framework that is a good fit to estimation methods that use ML techniques as black-boxes.
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation

Robust Statistics and Causal Inference

Real data always contain outliers or small "exceptional" sub-populations. Our lab develops techniques that can deal with such outliers in the context of causal estimation.
Taking a Moment for Distributional Robustness

Robust Generalized Method of Moments: A Finite Sample Viewpoint, NeurIPS22

Causal Inference and Incentives

Experimentation in real world systems, typically involves interacting with human subjects, each having their own incentives and trying to maximize their utility. Our lab develops experimentation and inference procedures that account for such incentives.
Switchback Price Experiments with Forward-Looking Demand, EC26 (EC'26 Accepted Papers)

Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration (Journal Version)

Incentivizing Compliance with Algorithmic Instruments, ICML21

Optimal Data Acquisition for Statistical Estimation, EC18

Learning from Experts

Expert demonstration data can augment ML algorithms either by informing adaptive experiments or by informing generative models of the human preferences that their outputs need to align with. Our lab develops learning algorithms that can leverage such datasets and improve learning or quality of models.
Personalized Adaptation via In-Context Preference Learning, Arxiv24

Direct Preference Optimization With Unobserved Preference Heterogeneity, Arxiv24

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity, NeurIPS24

Teaching

Applied Causal Inference Powered by ML and AI

Fundamentals of modern applied causal inference. Basic principles of causal inference and machine learning and how the two can be combined in practice to deliver causal insights and policy implications in real world datasets, allowing for high-dimensionality and flexible estimation. Lectures will provide foundations of these new methodologies and the course assignments will involve real world data (from social science, tech industry and healthcare applications) and synthetic data analysis based on these methodologies.

Explore the Applied Causal ML Book Course website

Foundations of Causal Machine Learning

Theoretical foundations of modern techniques at the intersection of causal inference and machine learning. Topics include: semi-parametric inference and semi-parametric efficiency, modern statistical learning theory, Neyman orthogonality and debiased machine learning, adaptive non-parametric estimation of conditional moment models, estimation and inference on heterogeneous treatment effects, causal inference and reinforcement learning, off-policy evaluation.

Explore the Foundations of Causal ML Book Course website

Game Theory, Data Science and AI

Introduction to game theory and its applications in data science and artificial intelligence. Topics include strategic decision making, equilibrium concepts, mechanism design, and computational approaches to solving games, with connections to machine learning and causal inference.

Course website

People

Syrgkanis
Vasilis Syrgkanis
Lab PI, Asst. Professor (MS&E)
Whitehouse
Justin Whitehouse
Postdoctoral Researcher
SAIL Fellow
Lars van der Laan
Lars van der Laan
Postdoctoral Researcher
Causal Science Center Fellow
Cheedambaram
Keertana Cheedambaram
PhD Student (MS&E)
Tan
Jiyuan Tan
PhD Student (MS&E)
Jin
Jikai Jin
PhD Student (ICME)
Lin
Shiangyi Lin
PhD Student (ICME)
Sawarni
Ayush Sawarni
PhD Student (MS&E)

Alumni

Yifan Wu
Yifan Wu
Visiting PhD Student (23-24)
Hui Lan
Hui Lan
PhD Student (grad. 2026); now at Netflix Research
Ravi Sojitra
Ravi Sojitra
PhD Student (grad. 2026); now at Google
Calvin Xu
Calvin Xu
Former Undergrad Researcher (CS)
Karthik Seetharaman
Karthik Seetharaman
Former Undergrad Researcher (Math)
Saanvi Chawla
Saanvi Chawla
Former Undergrad Researcher (CS)

Publications

Browse the lab’s research by year or topic. Search titles, authors, or venues.

2026

  1. Inference on the Value of Policy Improvement in Adaptive Experiments
    Justin Whitehouse, Emma Brunskill, Vasilis Syrgkanis, Arxiv26
  2. Measuring Gift Card Program Incrementality via Causal Data Fusion
    Justin Whitehouse, William Betz, Yan Zhang, Peter Coles, Ramesh Johari, Vasilis Syrgkanis, Arxiv26
  3. Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
    Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis, NeurIPS26
  4. CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
    Jiyuan Tan, Vasilis Syrgkanis, Arxiv26
  5. The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice
    Jikai Jin, Vasilis Syrgkanis, Arxiv26
  6. Partial Identification of Policy-Relevant Treatment Effects with Instrumental Variables via Optimal Transport
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, Arxiv26
  7. Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle
    Jiyuan Tan, Vasilis Syrgkanis, Arxiv26
  8. The Double Diagonal Estimator: Reducing Bias in Two-Sided Marketplace Experiments
    Saanvi Chawla, Vasilis Syrgkanis, EC26 (EC'26 Accepted Papers)
  9. CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
    Ayush Sawarni, Jiyuan Tan, Vasilis Syrgkanis, Arxiv26
  10. Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
    Justin Whitehouse, Ayush Sawarni, Vasilis Syrgkanis, Arxiv26
  11. Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation
    Jikai Jin, Vasilis Syrgkanis, Arxiv26
  12. Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
    Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade, ICML26 (oral)
  13. Learning Treatment Representations for Downstream Instrumental Variable Regression
    Shiangyi Lin, Hui Lan, Vasilis Syrgkanis, ICML26
  14. Policy Learning with Abstention
    Ayush Sawarni, Jikai Jin, Justin Whitehouse, Vasilis Syrgkanis, AISTATS26
  15. Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences
    Keertana Chidambaram, Karthik Vinary Seetharaman, Vasilis Syrgkanis, AISTATS26

2025

  1. Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing
    Justin Whitehouse, Morgane Austern, Vasilis Syrgkanis, Arxiv25
  2. Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
    Jikai Jin, Vasilis Syrgkanis, Sham Kakade, Hanlin Zhang, Arxiv25 + COLM2026
  3. It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
    Jikai Jin, Lester Mackey, Vasilis Syrgkanis, NeurIPS 2025
  4. Estimation of Treatment Effects in Extreme and Unobserved Data
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS 2025
  5. Preference Learning with Response Time
    Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis, NeurIPS 2025
  6. Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
    Jikai Jin, Vasilis Syrgkanis, COLT 2025
  7. Orthogonal Causal Calibration
    Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu, COLT 2025
  8. A Meta-learner for Heterogeneous Effects in Difference-in-Differences
    Hui Lan, Haoge Chang, Eleanor Dillon, Vasilis Syrgkanis, ICML 2025
  9. Detecting clinician implicit biases in diagnoses using proximal causal inference
    Kara Liu, Russ Altman, Vasilis Syrgkanis, Pacific Symposium on Biocomputing 2025

2024

  1. Predicting Long Term Sequential Policy Value Using Softer Surrogates
    Hyunji Nam, Allen Nie, Ge Gao, Vasilis Syrgkanis, Emma Brunskill, Arxiv24
  2. Conditional Influence Functions
    Victor Chernozhukov, Whitney K. Newey, Vasilis Syrgkanis, Arxiv24, Quantitative Economics
  3. Automatic Doubly Robust Forests
    Zhaomeng Chen, Junting Duan, Victor Chernozhukov, Vasilis Syrgkanis, Arxiv24
  4. Switchback Price Experiments with Forward-Looking Demand
    Yifan Wu, Ramesh Johari, Vasilis Syrgkanis, Gabriel Y. Weintraub, Arxiv24, EC26 (EC'26 Accepted Papers)
  5. Personalized Adaptation via In-Context Preference Learning
    Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan, Arxiv24
  6. Dynamic Local Average Treatment Effects
    Ravi Sojitra, Vasilis Syrgkanis, Arxiv24
  7. Simultaneous Inference for Local Structural Parameters with Random Forests
    David Ritzwoller, Vasilis Syrgkanis, Arxiv24
  8. Regularized DeepIV with Model Selection
    Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang, Masatoshi Uehara, Arxiv24
  9. Taking a Moment for Distributional Robustness
    Jabari Hastings, Christopher Jung, Charlotte Peale, Vasilis Syrgkanis, Arxiv24
  10. Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
    Jikai Jin, Vasilis Syrgkanis, NeurIPS24 (Spotlight)
  11. Consistency of Neural Causal Partial Identification
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS24
  12. Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity, NeurIPS24
    Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis, NeurIPS24
  13. Causal Q-Aggregation for CATE Model Selection
    Hui Lan, Vasilis Syrgkanis, AISTATS24
  14. Adaptive Instrument Design for Indirect Experiments
    Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill, ICLR24
  15. Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation
    Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis, ICLR24

2023

  1. Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
    Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu, Arxiv23, TMLR2026 (Journal Version)
  2. Inference on Optimal Dynamic Policies via Softmax Approximation
    Qizhao Chen, Morgane Austern, Vasilis Syrgkanis, Arxiv23
  3. Automatic Debiased Machine Learning for Covariate Shifts
    Victor Chernozhukov, Michael Newey, Whitney K Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv23, Biometrika (Journal Version)
  4. Post Reinforcement Learning Inference
    Ruohan Zhan, Vasilis Syrgkanis, Arxiv23, Operations Research 2025 (Journal Version)
  5. Source Condition Double Robust Inference on Functionals of Inverse Problems
    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, Arxiv23
  6. Inference on Strongly Identified Functionals of Weakly Identified Functions
    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, COLT23, JRSS-B (Journal Version)
  7. Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness
    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, COLT23

2022

  1. Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
    Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis, Haeyeon Yoon, Arxiv22
  2. Finding Subgroups with Significant Treatment Effects, CLear22
    Jann Spiess, Vasilis Syrgkanis, Victor Yaneng Wang, CLear22
  3. Non-Parametric Inference Adaptive to Intrinsic Dimension
    Khashayar Khosravi, Gregory Lewis, Vasilis Syrgkanis, CLear22
  4. Towards efficient representation identification in supervised learning Kartik Ahuja, Divyat Mahajan, Ioannis Mitliagkas, Vasilis Syrgkanis, CLear22
  5. Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
    Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis, The Econometrics Journal, 2022 (Journal Version)
  6. Automatic Debiased Machine Learning for Dynamic Treatment Effects
    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv22
  7. Robust Generalized Method of Moments: A Finite Sample Viewpoint, NeurIPS22
    Dhruv Rohatgi, Vasilis Syrgkanis, NeurIPS22
  8. Debiased Machine Learning without Sample-Splitting for Stable Estimators
    Qizhao Chen, Vasilis Syrgkanis, Morgane Austern, NeurIPS22
  9. Partial Identification of Treatment Effects with Implicit Generative Models
    Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G Krishnan, NeurIPS22 (Spotlight)
  10. RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
    Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis, ICML22 (Long Oral)

2021

  1. Long Story Short: Omitted Variable Bias in Causal Machine Learning
    Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis, Arxiv21, ReSTAT (Journal Version)
  2. Automatic Debiased Machine Learning via Riesz Regression
    Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis, Arxiv21
  3. Adversarial Estimation of Riesz Representers
    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv21, JASA (Journal Version)
  4. Estimating the Long-Term Effects of Novel Treatments
    Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis, NeurIPS21
  5. Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation
    Greg Lewis, Vasilis Syrgkanis, NeurIPS21
  6. Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection
    Morgane Austern, Vasilis Syrgkanis, NeurIPS2021
  7. DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
    Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman, ICML21 Workshop on the Neglected Assumptions in Causal Inference
  8. Dynamically Aggregating Diverse Information
    Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis, EC2021 and Econometrica, 2021 (Journal Version)
  9. Incentivizing Compliance with Algorithmic Instruments, ICML21
    Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu, ICML21
  10. Knowledge Distillation as Semi-Parametric Inference
    Tri Dao, Govinda Kamath, Vasilis Syrgkanis, Lester Mackey, ICLR21

2020

  1. Estimation and Inference with Trees and Forests in High Dimensions
    Vasilis Syrgkanis, Manolis Zampetakis, COLT20
  2. Minimax Estimation of Conditional Moment Models
    Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis, NeurIPS20

2019

  1. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis, NeurIPS19 (spotlight)
  2. Semi-Parametric Efficient Policy Learning with Continuous Actions
    Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov, NeurIPS19
  3. Low-rank Bandit Methods for High-dimensional Dynamic Pricing
    Jonas Mueller, Vasilis Syrgkanis, Matt Taddy, NeurIPS19
  4. Orthogonal Statistical Learning
    Dylan Foster, Vasilis Syrgkanis, COLT19 (Best Paper Award) and Annals of Statistics 2022 (Journal Version)
  5. Orthogonal Random Forest for Causal Inference
    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu, ICML19

2018

  1. Semiparametric Contextual Bandits
    Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis, ICML18
  2. Accurate Inference for Adaptive Linear Models
    Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy, ICML18
  3. Optimal Data Acquisition for Statistical Estimation
    Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, Juba Ziani, EC18
  4. Orthogonal Machine Learning: Power and Limitations
    Lester Mackey, Vasilis Syrgkanis, Ilias Zadik, ICML18

Heterogeneous Effects: Estimation, Inference and Policy Learning

  1. Policy Learning with Abstention
    Ayush Sawarni, Jikai Jin, Justin Whitehouse, Vasilis Syrgkanis, AISTATS26
  2. Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing
    Justin Whitehouse, Morgane Austern, Vasilis Syrgkanis, Arxiv25
  3. A Meta-learner for Heterogeneous Effects in Difference-in-Differences
    Hui Lan, Haoge Chang, Eleanor Dillon, Vasilis Syrgkanis, ICML 2025
  4. Conditional Influence Functions
    Victor Chernozhukov, Whitney K. Newey, Vasilis Syrgkanis, Arxiv24, Quantitative Economics
  5. Automatic Doubly Robust Forests
    Zhaomeng Chen, Junting Duan, Victor Chernozhukov, Vasilis Syrgkanis, Arxiv24
  6. Orthogonal Causal Calibration
    Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu, COLT 2025
  7. Simultaneous Inference for Local Structural Parameters with Random Forests
    Ritzwoller, Syrgkanis, Arxiv24
  8. Causal Q-Aggregation for CATE Model Selection
    Hui Lan, Vasilis Syrgkanis, AISTATS24
  9. Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation
    Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis, ICLR24
  10. Finding Subgroups with Significant Treatment Effects, CLear22
    Jann Spiess, Vasilis Syrgkanis, Victor Yaneng Wang, CLear22
  11. Non-Parametric Inference Adaptive to Intrinsic Dimension
    Khashayar Khosravi, Gregory Lewis, Vasilis Syrgkanis, CLear22
  12. Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
    Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis, The Econometrics Journal, 2022 (Journal Version)
  13. Estimation and Inference with Trees and Forests in High Dimensions
    Vasilis Syrgkanis, Manolis Zampetakis, COLT20
  14. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis, NeurIPS19 (spotlight)
  15. Semi-Parametric Efficient Policy Learning with Continuous Actions
    Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov, NeurIPS19
  16. Orthogonal Statistical Learning
    Dylan Foster, Vasilis Syrgkanis, COLT19 (Best Paper Award) and Annals of Statistics 2022 (Journal Version)
  17. Orthogonal Random Forest for Causal Inference
    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu, ICML19

Debiased Machine Learning

  1. Measuring Gift Card Program Incrementality via Causal Data Fusion
    Justin Whitehouse, William Betz, Yan Zhang, Peter Coles, Ramesh Johari, Vasilis Syrgkanis, Arxiv26
  2. Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
    Justin Whitehouse, Ayush Sawarni, Vasilis Syrgkanis, Arxiv26
  3. Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing
    Justin Whitehouse, Morgane Austern, Vasilis Syrgkanis, Arxiv25
  4. Estimation of Treatment Effects in Extreme and Unobserved Data
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS 2025
  5. Detecting clinician implicit biases in diagnoses using proximal causal inference
    Kara Liu, Russ Altman, Vasilis Syrgkanis, Pacific Symposium on Biocomputing 2025
  6. Predicting Long Term Sequential Policy Value Using Softer Surrogates
    Hyunji Nam, Allen Nie, Ge Gao, Vasilis Syrgkanis, Emma Brunskill, Arxiv24
  7. Source Condition Double Robust Inference on Functionals of Inverse Problems
    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, Arxiv23
  8. Automatic Debiased Machine Learning for Covariate Shifts
    Victor Chernozhukov, Michael Newey, Whitney K Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv23, Biometrika (Journal Version)
  9. Automatic Debiased Machine Learning for Dynamic Treatment Effects
    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv22
  10. Automatic Debiased Machine Learning via Riesz Regression
    Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis, Arxiv21
  11. Adversarial Estimation of Riesz Representers
    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, JASA 2025 (Journal Version)
  12. Inference on Strongly Identified Functionals of Weakly Identified Functions
    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, COLT23, JRSS-B (Journal Version)
  13. Debiased Machine Learning without Sample-Splitting for Stable Estimators
    Qizhao Chen, Vasilis Syrgkanis, Morgane Austern, NeurIPS22
  14. RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
    Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis, ICML22 (Long Oral)
  15. Knowledge Distillation as Semi-Parametric Inference
    Tri Dao, Govinda Kamath, Vasilis Syrgkanis, Lester Mackey, ICLR21
  16. Orthogonal Machine Learning: Power and Limitations
    Lester Mackey, Vasilis Syrgkanis, Ilias Zadik, ICML18

Non-Parametric Instrumental Variables

  1. Partial Identification of Policy-Relevant Treatment Effects with Instrumental Variables via Optimal Transport
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, Arxiv26
  2. Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle
    Jiyuan Tan, Vasilis Syrgkanis, Arxiv26
  3. Learning Treatment Representations for Downstream Instrumental Variable Regression
    Shiangyi Lin, Hui Lan, Vasilis Syrgkanis, Arxiv25
  4. Regularized DeepIV with Model Selection
    Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang, Masatoshi Uehara, Arxiv24
  5. Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness
    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, COLT23
  6. Minimax Estimation of Conditional Moment Models
    Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis, NeurIPS20
  7. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis, NeurIPS19 (spotlight)

Adaptive Experiments

  1. Inference on the Value of Policy Improvement in Adaptive Experiments
    Justin Whitehouse, Emma Brunskill, Vasilis Syrgkanis, Arxiv26
  2. Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity, NeurIPS24
    Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis, NeurIPS24
  3. Post Reinforcement Learning Inference
    Ruohan Zhan, Vasilis Syrgkanis, Operations Research 2025 (Journal Version)
  4. Adaptive Instrument Design for Indirect Experiments
    Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill, ICLR24
  5. Dynamically Aggregating Diverse Information
    Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis, EC2021 and Econometrica, 2021 (Journal Version)
  6. Low-rank Bandit Methods for High-dimensional Dynamic Pricing
    Jonas Mueller, Vasilis Syrgkanis, Matt Taddy, NeurIPS19
  7. Semiparametric Contextual Bandits
    Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis, ICML18
  8. Accurate Inference for Adaptive Linear Models
    Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy, ICML18

Dynamic Treatment Regimes

  1. Dynamic Local Average Treatment Effects
    Ravi Sojitra, Vasilis Syrgkanis, Arxiv24
  2. Inference on Optimal Dynamic Policies via Softmax Approximation
    Qizhao Chen, Morgane Austern, Vasilis Syrgkanis, Arxiv23
  3. Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
    Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis, Haeyeon Yoon, Arxiv22
  4. Automatic Debiased Machine Learning for Dynamic Treatment Effects
    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv22
  5. Estimating the Long-Term Effects of Novel Treatments
    Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis, NeurIPS21
  6. Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation
    Greg Lewis, Vasilis Syrgkanis, NeurIPS21

Neural Causal Models

  1. Consistency of Neural Causal Partial Identification
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS24
  2. Partial Identification of Treatment Effects with Implicit Generative Models
    Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G Krishnan, NeurIPS22 (Spotlight)

Causal Representation Learning

  1. Learning Treatment Representations for Downstream Instrumental Variable Regression
    Shiangyi Lin, Hui Lan, Vasilis Syrgkanis, Arxiv25
  2. Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
    Jikai Jin, Vasilis Syrgkanis, NeurIPS24 (Spotlight)
  3. Towards efficient representation identification in supervised learning Kartik Ahuja, Divyat Mahajan, Ioannis Mitliagkas, Vasilis Syrgkanis, CLear22

Sensitivity Analysis

  1. Long Story Short: Omitted Variable Bias in Causal Machine Learning
    Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis, ReSTAT (Journal Version)
  2. DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
    Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman, ICML21 Workshop on the Neglected Assumptions in Causal Inference

Large Language Models

  1. Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
    Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis, NeurIPS26
  2. CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
    Jiyuan Tan, Vasilis Syrgkanis, Arxiv26
  3. The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice
    Jikai Jin, Vasilis Syrgkanis, Arxiv26
  4. Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences
    Keertana Chidambaram, Karthik Vinary Seetharaman, Vasilis Syrgkanis, AISTATS26
  5. Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
    Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade, ICML26 (oral)
  6. Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
    Jikai Jin, Vasilis Syrgkanis, Sham Kakade, Hanlin Zhang, Arxiv25 + COLM2026
  7. CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
    Ayush Sawarni, Jiyuan Tan, Vasilis Syrgkanis, Arxiv26
  8. Preference Learning with Response Time
    Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis, NeurIPS 2025
  9. Personalized Adaptation via In-Context Preference Learning
    Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan, Arxiv24

Minimax Lower Bounds for Causal Estimation

  1. Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation
    Jikai Jin, Vasilis Syrgkanis, Arxiv26
  2. It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
    Jikai Jin, Lester Mackey, Vasilis Syrgkanis, NeurIPS 2025
  3. Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
    Jikai Jin, Vasilis Syrgkanis, COLT 2025

Robust Statistics and Causal Inference

  1. Estimation of Treatment Effects in Extreme and Unobserved Data
    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS 2025
  2. Taking a Moment for Distributional Robustness
    Jabari Hastings, Christopher Jung, Charlotte Peale, Vasilis Syrgkanis, Arxiv24
  3. Robust Generalized Method of Moments: A Finite Sample Viewpoint, NeurIPS22
    Dhruv Rohatgi, Vasilis Syrgkanis, NeurIPS22

Causal Inference and Incentives

  1. Switchback Price Experiments with Forward-Looking Demand
    Yifan Wu, Ramesh Johari, Vasilis Syrgkanis, Gabriel Y. Weintraub, Arxiv24, EC26 (EC'26 Accepted Papers)
  2. The Double Diagonal Estimator: Reducing Bias in Two-Sided Marketplace Experiments
    Saanvi Chawla, Vasilis Syrgkanis, EC26 (EC'26 Accepted Papers)
  3. Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
    Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu, Arxiv23 (Journal Version)
  4. Incentivizing Compliance with Algorithmic Instruments, ICML21
    Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu, ICML21
  5. Optimal Data Acquisition for Statistical Estimation
    Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, Juba Ziani, EC18

Learning from Experts

  1. Preference Learning with Response Time
    Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis, NeurIPS 2025
  2. Personalized Adaptation via In-Context Preference Learning
    Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan, Arxiv24
  3. Direct Preference Optimization With Unobserved Preference Heterogeneity
    Keertana Chidambaram, Karthik Vinay Seetharaman, Vasilis Syrgkanis, Arxiv24
  4. Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
    Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis, NeurIPS24

Stanford Causal AI Lab

Let’s connect

Contact us with questions about our research, collaborations, or teaching.

Partner with us

Want to partner with us on a particular problem? Submit an intake form.

Submit an intake form

Address

Huang 252, Huang Engineering Center
Stanford, CA 94305

Email the lab

vsyrgk@stanford.edu

For research, collaboration, and course inquiries