Yonghan Jung
@yonghanjung.bsky.social
Assistant professor at UIUC iSchool.
Previously at Purdue CS.
Work on Causal Data Science
https://yonghanjung.me/
Previously at Purdue CS.
Work on Causal Data Science
https://yonghanjung.me/
Pinned
Yonghan Jung
@yonghanjung.bsky.social
· Sep 29
Debiased Front-Door Learners for Heterogeneous Effects
In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the m...
arxiv.org
Thrilled to share our new paper!
📄 Paper: arxiv.org/abs/2509.22531
💻 Code: github.com/yonghanjung/...
We develop the first orthogonal ML estimators for heterogeneous treatment effects (HTE) under front-door adjustment, enabling HTE identification even with unmeasured confounders.
📄 Paper: arxiv.org/abs/2509.22531
💻 Code: github.com/yonghanjung/...
We develop the first orthogonal ML estimators for heterogeneous treatment effects (HTE) under front-door adjustment, enabling HTE identification even with unmeasured confounders.
Reposted by Yonghan Jung
Yonghan Jung: Debiased Front-Door Learners for Heterogeneous Effects https://arxiv.org/abs/2509.22531 https://arxiv.org/pdf/2509.22531 https://arxiv.org/html/2509.22531
September 29, 2025 at 6:53 AM
Yonghan Jung: Debiased Front-Door Learners for Heterogeneous Effects https://arxiv.org/abs/2509.22531 https://arxiv.org/pdf/2509.22531 https://arxiv.org/html/2509.22531
Thrilled to share our new paper!
📄 Paper: arxiv.org/abs/2509.22531
💻 Code: github.com/yonghanjung/...
We develop the first orthogonal ML estimators for heterogeneous treatment effects (HTE) under front-door adjustment, enabling HTE identification even with unmeasured confounders.
📄 Paper: arxiv.org/abs/2509.22531
💻 Code: github.com/yonghanjung/...
We develop the first orthogonal ML estimators for heterogeneous treatment effects (HTE) under front-door adjustment, enabling HTE identification even with unmeasured confounders.
Debiased Front-Door Learners for Heterogeneous Effects
In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the m...
arxiv.org
September 29, 2025 at 3:58 PM
Thrilled to share our new paper!
📄 Paper: arxiv.org/abs/2509.22531
💻 Code: github.com/yonghanjung/...
We develop the first orthogonal ML estimators for heterogeneous treatment effects (HTE) under front-door adjustment, enabling HTE identification even with unmeasured confounders.
📄 Paper: arxiv.org/abs/2509.22531
💻 Code: github.com/yonghanjung/...
We develop the first orthogonal ML estimators for heterogeneous treatment effects (HTE) under front-door adjustment, enabling HTE identification even with unmeasured confounders.
I'm excited to share that I'll be joining the School of Information Sciences at UIUC as an Assistant Professor this Fall (ischool.illinois.edu/news-events/...). If you're interested in causal inference and its applications to trustworthy AI and healthcare, join me & let's work together!
Jung to join the faculty
The iSchool is pleased to announce that Yonghan Jung will join the faculty as an assistant professor in August 2025, pending approval by the University of Illinois Board of Trustees.
ischool.illinois.edu
June 13, 2025 at 8:28 PM
I'm excited to share that I'll be joining the School of Information Sciences at UIUC as an Assistant Professor this Fall (ischool.illinois.edu/news-events/...). If you're interested in causal inference and its applications to trustworthy AI and healthcare, join me & let's work together!
PhDone 🎓 I’ve successfully defended my thesis!
Huge thanks to my amazing advisor Elias Bareinboim and committee—Jennifer Neville, Jin Tian, Yexiang Xue, and @idiaz.bsky.social.
Grateful to collaborators, colleagues, lab mates, friends, neighbors—and above all, my wife, kid, and family!
Huge thanks to my amazing advisor Elias Bareinboim and committee—Jennifer Neville, Jin Tian, Yexiang Xue, and @idiaz.bsky.social.
Grateful to collaborators, colleagues, lab mates, friends, neighbors—and above all, my wife, kid, and family!
June 10, 2025 at 7:40 PM
PhDone 🎓 I’ve successfully defended my thesis!
Huge thanks to my amazing advisor Elias Bareinboim and committee—Jennifer Neville, Jin Tian, Yexiang Xue, and @idiaz.bsky.social.
Grateful to collaborators, colleagues, lab mates, friends, neighbors—and above all, my wife, kid, and family!
Huge thanks to my amazing advisor Elias Bareinboim and committee—Jennifer Neville, Jin Tian, Yexiang Xue, and @idiaz.bsky.social.
Grateful to collaborators, colleagues, lab mates, friends, neighbors—and above all, my wife, kid, and family!
Reposted by Yonghan Jung
New paper alert (hey, I can't doom scroll all the time): This one's on doing causal inference with "microlevel data" where we suspect that the treatment has spatial spillover & temporal carryover effects. We illustrate our new approach + package w/ application to US counterinsurgency efforts in Iraq
Spatiotemporal causal inference with arbitrary spillover and carryover effects
Micro-level data with granular spatial and temporal information are becoming increasingly available to social scientists. Most researchers aggregate such data into a convenient panel data format and a...
arxiv.org
April 7, 2025 at 11:58 PM
New paper alert (hey, I can't doom scroll all the time): This one's on doing causal inference with "microlevel data" where we suspect that the treatment has spatial spillover & temporal carryover effects. We illustrate our new approach + package w/ application to US counterinsurgency efforts in Iraq
📌Interesting way of using copula method for the sensitivity analysis in causal inference.
link 📈🤖
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding (Zheng, D'Amour, Franks) Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous w
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding (Zheng, D'Amour, Franks) Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous w
March 30, 2025 at 3:55 PM
📌Interesting way of using copula method for the sensitivity analysis in causal inference.
Reposted by Yonghan Jung
Reinforcement learning has led to amazing breakthroughs in reasoning (e.g., R1), but can it discover truly new behaviors not already present in the base model?
A new paper with Zak Mhammedi and Dhruv Rohatgi:
The Computational Role of the Base Model in Exploration
arxiv.org/abs/2503.07453
A new paper with Zak Mhammedi and Dhruv Rohatgi:
The Computational Role of the Base Model in Exploration
arxiv.org/abs/2503.07453
March 27, 2025 at 5:28 PM
Reinforcement learning has led to amazing breakthroughs in reasoning (e.g., R1), but can it discover truly new behaviors not already present in the base model?
A new paper with Zak Mhammedi and Dhruv Rohatgi:
The Computational Role of the Base Model in Exploration
arxiv.org/abs/2503.07453
A new paper with Zak Mhammedi and Dhruv Rohatgi:
The Computational Role of the Base Model in Exploration
arxiv.org/abs/2503.07453
It looks interesting!
link 📈🤖
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data () This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation. We propose a neural network architecture that is adapted to the double machine lear
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data () This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation. We propose a neural network architecture that is adapted to the double machine lear
March 21, 2025 at 3:06 PM
It looks interesting!
I really enjoy reading this paper. On a perspective of causal inference researcher, I agree that ML's real-world impact relies on science theory, because understanding causal mechanisms requires domain knowledge or theoretical assumptions. ML without theory simply leads us nowhere.
My own take on this “theory-free ideal” philsci-archive.pitt.edu/23840/
The Devil in the Data: Machine Learning & the Theory-Free Ideal - PhilSci-Archive
philsci-archive.pitt.edu
March 10, 2025 at 6:11 PM
I really enjoy reading this paper. On a perspective of causal inference researcher, I agree that ML's real-world impact relies on science theory, because understanding causal mechanisms requires domain knowledge or theoretical assumptions. ML without theory simply leads us nowhere.
Reposted by Yonghan Jung
link 📈🤖
Adaptive Experimentation When You Can't Experiment () arXiv:2406.10738v1 Announce Type: cross
Abstract: This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly assig
Adaptive Experimentation When You Can't Experiment () arXiv:2406.10738v1 Announce Type: cross
Abstract: This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly assig
February 22, 2025 at 1:32 AM
link 📈🤖
Adaptive Experimentation When You Can't Experiment () arXiv:2406.10738v1 Announce Type: cross
Abstract: This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly assig
Adaptive Experimentation When You Can't Experiment () arXiv:2406.10738v1 Announce Type: cross
Abstract: This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly assig
Reposted by Yonghan Jung
👉 Join our #CIIG seminar next month for an Introduction to Mechanism Learning
👉 Mechanism learning proposes using front-door causal bootstrapping such that ML models learn causal rather than "associational" (or spurious) relationships
See abstract and register: turing-uk.zoom.us/meeting/regi...
👉 Mechanism learning proposes using front-door causal bootstrapping such that ML models learn causal rather than "associational" (or spurious) relationships
See abstract and register: turing-uk.zoom.us/meeting/regi...
January 29, 2025 at 2:08 PM
👉 Join our #CIIG seminar next month for an Introduction to Mechanism Learning
👉 Mechanism learning proposes using front-door causal bootstrapping such that ML models learn causal rather than "associational" (or spurious) relationships
See abstract and register: turing-uk.zoom.us/meeting/regi...
👉 Mechanism learning proposes using front-door causal bootstrapping such that ML models learn causal rather than "associational" (or spurious) relationships
See abstract and register: turing-uk.zoom.us/meeting/regi...
Reposted by Yonghan Jung
@pedrosantanna.bsky.social onlinelibrary.wiley.com/doi/10.1111/... biostatistics literature will use PO notation to describe the relevant objects. Just treat RL as MDP with unknown transitions (it's true RL doesn't use PO notation - it gets cumbersome and many key objects relate to the Bellman eqn)
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delivering adaptive inter....
onlinelibrary.wiley.com
January 13, 2025 at 12:55 AM
@pedrosantanna.bsky.social onlinelibrary.wiley.com/doi/10.1111/... biostatistics literature will use PO notation to describe the relevant objects. Just treat RL as MDP with unknown transitions (it's true RL doesn't use PO notation - it gets cumbersome and many key objects relate to the Bellman eqn)
Reposted by Yonghan Jung
I've decided to collect my DiD materials in a single place.
psantanna.com/did-resources
There, you will find
- 14 lectures of my comprehensive DiD course
- Shorter lectures/talks I have given on DiD
- My DiD R/Stata/Python packages
- Some DiD checklists
- DiD materials from my friends
Enjoy!
psantanna.com/did-resources
There, you will find
- 14 lectures of my comprehensive DiD course
- Shorter lectures/talks I have given on DiD
- My DiD R/Stata/Python packages
- Some DiD checklists
- DiD materials from my friends
Enjoy!
Pedro H. C. Sant’Anna
psantanna.com
January 3, 2025 at 4:44 PM
I've decided to collect my DiD materials in a single place.
psantanna.com/did-resources
There, you will find
- 14 lectures of my comprehensive DiD course
- Shorter lectures/talks I have given on DiD
- My DiD R/Stata/Python packages
- Some DiD checklists
- DiD materials from my friends
Enjoy!
psantanna.com/did-resources
There, you will find
- 14 lectures of my comprehensive DiD course
- Shorter lectures/talks I have given on DiD
- My DiD R/Stata/Python packages
- Some DiD checklists
- DiD materials from my friends
Enjoy!
Merry Christmas, friends and colleagues! Hope you all have wonderful days with joys! 🎄
December 25, 2024 at 9:49 PM
Merry Christmas, friends and colleagues! Hope you all have wonderful days with joys! 🎄
Excited to share that I’m on the academic job market! I’ve been fortunate to work with Elias Bareinboim on causal inference, developing causal effect estimators using modern ML methods. Published in ICML, NeurIPS, AAAI, & more. Details: www.yonghanjung.me
CausalAI Aficionado
Yonghan Jung
www.yonghanjung.me
December 19, 2024 at 6:46 PM
Excited to share that I’m on the academic job market! I’ve been fortunate to work with Elias Bareinboim on causal inference, developing causal effect estimators using modern ML methods. Published in ICML, NeurIPS, AAAI, & more. Details: www.yonghanjung.me
We will present our work "Unified Covariate Adjustment for Causal Inference” (joint work with Jin Tian &
Elias Bareinboim) at #NeurIPS2024!
- Wed (12/11) from 11am - 2pm
- Poster Session 1 (East Hall A-C) #4901
- Link: openreview.net/pdf?id=aX9z2...
Come and see us!
Elias Bareinboim) at #NeurIPS2024!
- Wed (12/11) from 11am - 2pm
- Poster Session 1 (East Hall A-C) #4901
- Link: openreview.net/pdf?id=aX9z2...
Come and see us!
December 11, 2024 at 6:11 PM
We will present our work "Unified Covariate Adjustment for Causal Inference” (joint work with Jin Tian &
Elias Bareinboim) at #NeurIPS2024!
- Wed (12/11) from 11am - 2pm
- Poster Session 1 (East Hall A-C) #4901
- Link: openreview.net/pdf?id=aX9z2...
Come and see us!
Elias Bareinboim) at #NeurIPS2024!
- Wed (12/11) from 11am - 2pm
- Poster Session 1 (East Hall A-C) #4901
- Link: openreview.net/pdf?id=aX9z2...
Come and see us!
I am attending NeurIPS 2024 this Tuesday through Sunday. I am also in the academic job market this year (www.yonghanjung.me). Happy to discuss potential opportunities! Get in touch if you’d like to chat! #NeurIPS2024
December 10, 2024 at 11:20 PM
I am attending NeurIPS 2024 this Tuesday through Sunday. I am also in the academic job market this year (www.yonghanjung.me). Happy to discuss potential opportunities! Get in touch if you’d like to chat! #NeurIPS2024
Reposted by Yonghan Jung
Kandiros, Pipis, Daskalakis, and Harshaw have a really Interesting new arxiv preprint on "conflict graph designs" for interference/spillovers: arxiv.org/abs/2411.10908 For GATE estimation the improvement is very significant and I'm optimistic/excited about how the ideas will impact the literature..!
November 22, 2024 at 1:51 PM
Kandiros, Pipis, Daskalakis, and Harshaw have a really Interesting new arxiv preprint on "conflict graph designs" for interference/spillovers: arxiv.org/abs/2411.10908 For GATE estimation the improvement is very significant and I'm optimistic/excited about how the ideas will impact the literature..!
Reposted by Yonghan Jung
As my first post on this platform, allow me to advertise the RL theory lecture notes I have been developing with Sasha Rakhlin: arxiv.org/abs/2312.16730
(shameless repost of my pinned tweet)
(shameless repost of my pinned tweet)
November 21, 2024 at 2:48 PM
As my first post on this platform, allow me to advertise the RL theory lecture notes I have been developing with Sasha Rakhlin: arxiv.org/abs/2312.16730
(shameless repost of my pinned tweet)
(shameless repost of my pinned tweet)
Reposted by Yonghan Jung
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits https://arxiv.org/abs/2311.05794 arXiv:2311.05794v2 Announce Type: replace Abstract: In multi-armed bandit (MAB) experiments, it is often advantageous to continuously produce inference on the average treatment effec 📈🤖
September 11, 2024 at 7:04 PM
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits https://arxiv.org/abs/2311.05794 arXiv:2311.05794v2 Announce Type: replace Abstract: In multi-armed bandit (MAB) experiments, it is often advantageous to continuously produce inference on the average treatment effec 📈🤖
Reposted by Yonghan Jung
Susan Athey, Raj Chetty, Guido Imbens, Hyunseung Kang
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
https://arxiv.org/abs/1603.09326
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
https://arxiv.org/abs/1603.09326
April 4, 2024 at 4:11 AM
Susan Athey, Raj Chetty, Guido Imbens, Hyunseung Kang
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
https://arxiv.org/abs/1603.09326
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
https://arxiv.org/abs/1603.09326
Reposted by Yonghan Jung
Siyu Heng, Jiawei Zhang, Yang Feng
Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
https://arxiv.org/abs/2310.18556
Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
https://arxiv.org/abs/2310.18556
April 4, 2024 at 4:12 AM
Siyu Heng, Jiawei Zhang, Yang Feng
Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
https://arxiv.org/abs/2310.18556
Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
https://arxiv.org/abs/2310.18556
Reposted by Yonghan Jung
Sizhu Lu, Zhichao Jiang, Peng Ding
Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation
https://arxiv.org/abs/2309.12425
Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation
https://arxiv.org/abs/2309.12425
April 4, 2024 at 4:12 AM
Sizhu Lu, Zhichao Jiang, Peng Ding
Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation
https://arxiv.org/abs/2309.12425
Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation
https://arxiv.org/abs/2309.12425
Reposted by Yonghan Jung
Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
A Double Machine Learning Approach to Combining Experimental and Observational Data
https://arxiv.org/abs/2307.01449
A Double Machine Learning Approach to Combining Experimental and Observational Data
https://arxiv.org/abs/2307.01449
April 4, 2024 at 4:12 AM
Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
A Double Machine Learning Approach to Combining Experimental and Observational Data
https://arxiv.org/abs/2307.01449
A Double Machine Learning Approach to Combining Experimental and Observational Data
https://arxiv.org/abs/2307.01449
Reposted by Yonghan Jung
Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding https://arxiv.org/abs/2305.17643 arXiv:2305.17643v2 Announce Type: replace Abstract: Causal inference with observational studies often suffers from unmeasured confounding, yielding biase 📈🤖
April 1, 2024 at 5:05 PM
Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding https://arxiv.org/abs/2305.17643 arXiv:2305.17643v2 Announce Type: replace Abstract: Causal inference with observational studies often suffers from unmeasured confounding, yielding biase 📈🤖