CTML - Center for Targeted Machine Learning and Causal Inference

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CTML, at UC Berkeley, is an interdisciplinary research center for advancing, implementing, and disseminating methodology to address problems arising in public health and clinical medicine. https://linktr.ee/ctml_ucberkeley

Join us for the Biostatistics & Epidemiology Research Showcase. Come hear our brilliant presenters share their cutting-edge research in fast-paced, engaging sessions. Check out the incredible lineup of speakers and their titles below! Date: Friday, May 1, 2026 Time: 2:30 PM - 4:30 PM Location: Berkeley Way West, 1st Floor | Room 1102 Presenters: Kaitlyn Lee - Improving Precision through Covariate Adjustment in RCTs with Binary Outcomes Nick Williams - Improving Reproducibility by Controlling Random Seed Stability in Machine Learning Based Estimation via Bagging John Halifax - Estimating Causal Effects of Community Drivers of Individual Health in the Presence of Community-Specific Confounding in Large Data Settings Alissa Gordon - The Average Mixed Derivative: A Nonparametric Framework of Interactivity Joy Nakato - Choices and Consequences of Alternative Estimands in Time-to-event Safety Analyses Noel Pimentel - MASH Placebo-Arm Database Project
Calling all UC Berkeley Biostatistics and Epidemiology graduate students! Got exciting research to share? Join us for the Biostatistics and Epidemiology Research Showcase—your chance to present to faculty, peers, and special guests! This event will take place on Friday, May 1st from 2:30PM to 4:30PM at Berkeley Way West, 1st Floor, Room 1102 & 1104. Choose your format: ⚡ Lightning Talk (3-min + Q&A) 📌 Poster Presentation 💡 Or do both! Visit our bio for full details on presentation formats 🔗 📣 Interested in presenting? Email Jessica Angell (jessica.angell@berkeley.edu) to sign up. #BerkeleyBiostat #BerkeleyEpi #BerkeleyCTML #BerkeleyPublicHealth #ResearchShowcase
Don't miss the next session of the CTML - Center for Targeted Machine Learning and Causal Inference Seminar Series on March 4th, where CTML GSR Alissa Gordon will discuss "Average Mixed Derivative: A Nonparametric Framework of Interactivity." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.
Hear from a leading expert in the field! Join us next Wednesday, February 25th for our Center for Targeted Machine Learning and Causal Inference (CTML) Seminar with Andrew Mertens, presenting "Spatial Superlearner for Subnational Micronutrient Deficiency Prediction and Proxy Identification in Data-sparse Settings." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. *Speaker will present remotely.*
Next week’s seminar brings another thought-provoking discussion! Join us on February 11th to hear from CTML GSR Mingxun (Michael) Wang presenting his talk on "Highly Adaptive Principal Component Regression: Fast HAL/HAR via Outcome-Blind Kernel PCA." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.
We’re pleased to welcome Toru Shirakawa, CTML GSR and CPH PhD Student, to next week’s CTML Seminar on Wednesday, February 4th, presenting on “A Conformalized Inference on Unobservable Variables.”The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. Abstract: Quantifying uncertainty in predicted unobservable variables is a critical area of research in statistics, artificial intelligence, and empirical science. Most scientific studies assume a specific structure involving unobservable variables for the data-generating process and draw inferences from a parameter of interest within that framework. Conformal prediction is a popular model-agnostic method for constructing prediction intervals for new observations. However, it typically requires observed true labels to build the prediction interval, making it unsuitable for unobserved latent variables. We propose a method to construct a prediction interval by leveraging sample-splitting of the training data and analyzing the discrepancy between two independently trained models. To ensure the identifiability of the distribution of this conformity score, we introduce a few assumptions regarding the distribution of the residuals of the predictions. Furthermore, we propose a residual orthogonalization to satisfy these assumptions with a coordinating regularization term. The performance of the proposed method was evaluated using both simulation and large language model experiments.
As we gear up in anticipation for this wonderful workshop, we'd like to highlight it once again to give every member of our community a chance to participate! CTML Co-Director Mark van der Laan’s talk, "The Causal Roadmap, Targeted Learning and TMLE: What Is That All About?", will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop this Thursday, on January 29th, 2026, from 10:00 AM–11:00 AM (EST). The talk will be available online via Zoom. Click the link in our bio to learn more about the talk and register today!
Connect with the Center for Targeted Machine Learning and Causal Inference (CTML) community! Next week’s seminar on Wednesday, January 28th, will feature CTML GSR Kaitlyn Lee presenting her talk, "Improving Precision through Covariate Adjustment in RCTs with Binary Outcomes." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. Please note that this week’s session will be open exclusively to the CTML and UC Berkeley community.
The Center for Targeted Machine Learning and Causal Inference (CTML) Spring Seminar Series kicks off next Wednesday, January 21, with a talk by CTML GSR Andy Kim: “Predicting Loss to Follow-Up Under Resource Constraints: Leveraging Registry-Linked Mobile Health Data in Trauma Care.” Join us at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.
We’re excited to kick off our first newsletter for the Spring 2026 semester coming next Wednesday, January 21! Subscribe today to stay in the loop with the CTML community: http://eepurl.com/iLgQIw
Huge thank you to Dr. Alejandro Schuler, Assistant Professor in Residence at UC Berkeley Biostatistics and CTML faculty member, for his continued efforts with CTML and for his talk at the Gilead Health Equity Partnership (GHEP) Seminar Series titled " Increasing the Efficiency of Randomized Trials with Machine Learning." Stay tuned for more cutting-edge conversations in our ongoing CTML × Gilead Health Equity Partnership (GHEP) Series in the coming weeks and on our website!
Explore everything CTML - Center for Targeted Machine Learning and Causal Inference has planned for our Spring Seminar Series! The full schedule is available by scanning the QR code on this post or by clicking the link in our bio. This spring, you can look forward to an engaging lineup of events—including a career panel featuring professionals in both biostatistics and epidemiology, as well as a panel discussion with experts from Blue Shield who will share insights from industry practice. Join us starting January 21st, 2026 to learn, connect, and grow with the CTML community!
CTML Co-Director Mark van der Laan’s talk will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop on Jan 29th, 2026, from 10AM–11AM (EST). The talk will be available online via Zoom. Click the link to register today! 🔗: https://www.phuse-events.org/attend/frontend/reg/tOtherPage.csp?pageID=80014&eventID=106&traceRedir=4
Former CTML Postdoc David McCoy will be presenting his and CTML Postdoc Zach Butzin-Dozier's research at the Neural Information Processing Systems (NeurIPS) 2025 Conference in Mexico City. If attending the conference, please stop by and check out their poster!
CTML Postdoc Marie Charpignon, will be presenting her poster at the AHLI 2025 ML4H Symposium next Monday, December 1st. If you’ll be attending AHLI, we welcome you to stop by Poster #4 to learn more about her work on federated target trial emulation using EHR data spanning multiple health systems!
Continuing our CTML Seminar Series is CTML GSR, Yi Li. His talk, "Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks" will take place on October 29th at 12:00PM at Berkeley Way West, 5th Floor, Room 5401. You won't want to miss it!
Center for Targeted Machine Learning and Causal Inference (CTML) at DahShu Data Science Symposium 2025! Mark van der Laan, CTML Faculty, and Sky Qiu, CTML Graduate Student Researcher, will be presenting a full-day short course titled "Highly Adaptive Lasso and Targeted Learning" at the DahShu Data Science Symposium 2025: Innovative Frontiers – AI and Data-Driven Advances in Drug Development, Precision Medicine, and Healthcare. Saturday, October 18th 9:00 AM – 5:00 PM This short course introduces the Highly Adaptive Lasso (HAL) and its role in advancing Targeted Learning, a general framework for constructing asymptotically linear, efficient estimators under realistic assumptions about the data generating process. Attendees will learn about HAL’s theoretical properties, its implementation for causal inference in R, and new developments including A-TMLE, P-TMLE, and DeepLTMLE with applications to modern challenges in drug development and precision medicine. Make sure to catch their course during the symposium!
The next talk in our CTML Seminar Series is coming up on October 15th! Join us for an engaging discussion led by Michael Rosenblum, Ph.D.(link is external), Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health, on "Methodological Problems in Every Black-Box Study of Forensic Firearm Comparisons." This talk will take place at 12:00PM at Berkeley Way West, 5th Floor, Room 5401.
T-shirt only: We’re reopening Professor Art Reingold’s commemorative t-shirt store for a limited time! Celebrate his remarkable career and legacy with an exclusive t-shirt designed just for this occasion. Link in bio to order! The store will close by October 25th.
Don't miss the next session of the CTML Seminar Series on October 1st, where Wenxin Zhang will discuss "Efficient Statistical Estimation for Sequential Adaptive Experiments with Implications for Adaptive Designs." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.
We are excited to announce CTML faculty, students and staff will lead roundtable discussions at the upcoming RISW (Regulatory Industry Statistic Workshop) at the ASA (American Statistical Association) conference in Rockville, Maryland! Date: September 25, 2025 Time: 11:45 AM – 1:00 PM (EDT)
The Center for Targeted Machine Learning and Causal Inference (CTML) Seminar Series continues on September 10th! Join us for an exciting talk on "Machine Learning, Causal Queueing, and SiMLQ for Data Driven Simulation." Opher Baron, Professor of Operations Management at the Rotman School of Management, University of Toronto, will be presenting joint work with Zhenghang Xu, a fifth-year PhD candidate in Operations Management and Statistics. This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.
Join us on September 3rd to kick off our Fall 2025 CTML Seminar Series! Adam Yala, Assistant Professor in Computational Precision Health, Statistics, and Computer Science (EECS), will start our series with his talk "AI for Personalized Cancer Care." This talk will take place at 12:00PM at Berkeley Way West, 5th Floor, Room 5401. Early detection significantly improves outcomes across many cancers, motivating major investments in population-wide screening programs, such as low-dose CT for lung cancer. To make screening more effective, we must simultaneously improve early detection for patients who will develop cancer while minimizing the harms of over screening. Advancing this Pareto frontier requires progress across three fronts: (1) accurately predicting patient outcomes from all available data, (2) designing intervention strategies tailored to risk, and (3) evaluating and translating these strategies into clinical practice. In this talk, I will present ongoing work across all three areas, driven by the goal of using every available bit of patient data to personalize care.
CTML Postdoc Marie-Laure Charpignon will be presenting during the poster session at the upcoming ISCB Conference on Wednesday, August 27. If you are attending ISCB, be sure to stop by the session and show your support!
Stay Connected with CTML! From cutting-edge publications and upcoming events to exciting achievements in our community, our newsletter keeps you in the loop. Subscribe today! Scan QR code or click the link in our bio to join.
There’s still time to be part of the tribute to Prof. Art Reingold! Link to the online store and graphics are in our bio. Help us spread the word. If a friend or colleague isn’t on social media, just send us their email via direct message and we’ll send them all the details!
Honoring a Global Public Health Leader: Professor Art Reingold. Join us in honoring the remarkable career of Professor Art Reingold. Art Reingold has shaped generations of public health leaders, with a transformative impact on global health. We're putting together a special memory book to thank him and honor his legacy. Join the Tribute: Commemorative T-Shirt for Art! 1. Buy a T-shirt Online store open until August 22nd! Option 1: "I, Too, Was Art's Student" Link to online store : https://www.customink.com/g/rgc0-00cz-cs3n Link to graphic: https://tinyurl.com/mrna8rf9 Option 2: "I, Too, Was Trained by Art's Student" Link to online store : https://www.customink.com/g/rgc0-00cz-cs7j Link to graphic: https://tinyurl.com/2hbrc7ac 2. Share a Photo Take a photo wearing your t-shirt—or holding his graphic—and snap a picture! 3. Be Part of the Legacy Book Submit your photo and testimony by Sept 30th to be included in a legacy book we're creating for Art!
Mark your calendars: CTML graduate student researchers Wenxin Zhang, Kaiwen Hou, and Alissa Gordon will be presenting their work next week at the 2025 Joint Statistical Meetings (JSM), held August 2–7 in Nashville, Tennessee. Innovations in causal inference, adaptive study design, and statistical methodology will be highlighted. If you’re attending JSM, don’t miss their sessions—come show your support!
CTML Visiting Student Researcher alum Carlos Garcia Meixide and CTML co-director Mark van der Laan present a new approach to causal inference. The new paper challenges the traditional assumption-based identification to what can be learned directly from observed data using implied interventions. By leveraging instrumental variable structure and Highly Adaptive Lasso (Hal), the authors propose a transparent G-computation formula that identifies causal effects without needing to specify everything up front.