Causal Mediation Analysis Training

Mediation analysis is an emerging field in causal inference relevant for comparative effectiveness research, evaluating and improving policy recommendations, and explaining biological mechanisms. Training in the potential outcomes framework for causal inference is important to understand the assumptions required for valid mediation analyses. This course will equip participants with foundational concepts and cutting edge statistical tools to investigate mediating mechanisms.

This three-day intensive course will cover some of the recent developments in causal mediation analysis and provide practical tools to implement these techniques and assess the mechanisms and pathways by which causal effects operate. Led by a team of experts in causal mediation techniques at Columbia University, this course will integrate lectures and discussion with hands-on computer lab sessions using R. The course will cover the relationship between traditional methods for mediation in environmental health, epidemiology, and the social sciences and new methods in causal inference using a wide variety of examples to illustrate the techniques and approaches. We will discuss 1) when the standard approaches to mediation analysis are valid for dichotomous, and continuous, outcomes, 2) alternative mediation analysis techniques when the standard approaches will not work, introducing the counterfactual notation for mediation analysis and formal definitions of natural direct and indirect effects, 3) the no-unmeasured confounding assumptions needed to identify these effects, and 4) how regression approaches for mediation analysis can be extended in the presence of multiple mediators.

By the end of the workshop, participants will be able to:

• Understand when traditional methods for mediation fail
• Articulate concepts about mediation under the counterfactual framework and assumptions for identification
• Formulate and apply regression approaches for mediation for single and multiple mediators
• Develop facility with the use of software for mediation and interpretation of software output

Summer 2026: The Causal Mediation Analysis Training is a livestream, remote training that takes place over live, online video on May 27-29, 2026 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.

Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are four requirements to attend this training:

1. Each participant must be familiar with linear and logistic regression.
2. Each participant must have experience with programming in R.
3. Although the instructors will provide an overview of the fundamentals of causal inference (potential outcomes, directed acyclic graphs, and marginal structural models), we invite the participants to read chapters 1-7, 11, and 12 of Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC (free).
4. Each participant is required to have a personal laptop/computer and a free, basic Posit Cloud (formerly RStudio Cloud) account. All lab sessions will be done using Posit Cloud (formerly RStudio Cloud).

Knowing basic R platform and commands is required for the training as noted in prerequisites above. This training will use Posit Cloud (formerly RStudio Cloud). If you are new to R or need a refresher, you can review the below tutorials to be well prepared:

• R Programming Tutorial - Learn the Basics: A free datalab.cc class on R fundamentals
• Once you create your free, basic Posit Cloud (formerly RStudio Cloud) account for the training: Primers on Programming Basics and Visualization Basics.
• SHARP Program Posit Cloud Tutorial: This self-paced tutorial from the Columbia SHARP Program walks through the Cloud Platform you will use at the training, as well as some basic exercises. We recommend this tutorial if you have not used the Cloud version of Posit (formerly RStudio) before, or if you are a beginner user of R.
• If you have any specific questions about R and R studio in the context of the Causal Mediation Analysis Training, please email us.

Linda Valeri, PhD, Mailman School of Public Health, Columbia University. Linda Valeri, is an Assistant Professor of Biostatistics at Columbia University Mailman School of Public Health. Dr. Valeri is an expert in causal inference with a focus on statistical methods and computational tools for causal mediation analysis, measurement error, and missing data. She is currently working on methods for mediation analysis with high-dimensional exposures, as well as intensive longitudinal and time-to-event data on mediators in the presence of competing risks. She is interested in translating statistical methods in public health and precision medicine to improve our understanding of mental health, environmental determinants of health, and health disparities. Dr. Valeri is also a passionate teacher. In the past ten years she has been teaching full semester as well as short courses on causal mediation analysis at premier academic institutions such as Columbia University, Harvard University, University of Michigan, Erasmus Universiteit Rotterdam (Netherlands), Universite’ de Bordeaux (France), and University of Milan (Italy). 

Training scholarships are available for the Causal Mediation Training.

"Excellent team, super knowledgeable. The sessions were very well organized, the materials were super on point, and the statistical code was really helpful. The instructor was extremely knowledgeable, patient, and kind." - Senior Social Sciences Data Analyst at Boston College Graduate School of Social Work, 2025

"This course tackles concepts that are not immediately intuitive, but the instructing team is incredibly effective in their delivery of difficult content." - PhD Candidate at Medical University of South Carolina, 2025

"Dr. Valeri and the instructional team did an exceptional job making complex concepts accessible without oversimplifying, creating an environment where both novice and advanced learners could thrive." - Faculty Member at Thomas Jefferson University College of Nursing, 2025

"I learned a lot and really appreciated how Caleb and Linda were willing to engage with all our questions, the extra time they put into answering questions in the Google Doc after class, and how they offered to help us in the future if we have questions. They were amazing and I am grateful they shared their knowledge with us through this course!" - Staff Member at Louisiana Department of Health, 2024

"This training was an succinct walk through of casual mediation philosophy and methods relevant to population health scholars. This training provides an extensive level of knowledge in 3 days that feels like weeks of training." - Graduate Student Researcher at University of Washington, 2024

"This course is both a great introduction and refresher to causal inference and mediation analysis. The instructors are very knowledgeable and the course has helped set me up with a strong foundation to explore causal inference and mediation analyses moving forward." - Postdoc at Stanford University School of Medicine, 2024

"This training has been incredibly enlightening and engaging. I will never look at DAGs the same way again." - PhD student at Columbia University, 2023

"This workshop provides a solid foundation in novel mediation methods, highlighting where they fill in the gaps left from traditional methods. I look forward to applying these tools to my own research!" - PhD student at Michigan State University, 2023

"An excellent theoretical and practical grounding in how to approach estimation of mediation and interaction in epidemiologic studies." - Environmental Epidemiologist at NYC Department of Health and Mental Hygiene, 2023

"The three-day training program provided an extensive review of causal mediation, and the workshop itself proved to be highly engaging. Additionally, the reference materials and accompanying R code proved to be valuable resources." - Student at Columbia University, 2023

Registration Fee is based on your category and includes course material, which will be made available to all participants both during and after the conclusion of the training.

2026 Registration Category Rates: 

• Student/Postdoc/Trainee: 
  • Early-bird rate: $1,295 
  • Regular rate: $1,495
• Faculty/Academic Staff/Non-Profit Organizations/Government Agencies: 
  • Early-bird rate: $1,495
  • Regular rate: $1,695
• Corporate/For-Profit Organizations: 
  • Early-bird rate: $1,695
  • Regular rate: $1,895

$200 early-bird discount is automatically applied if you register before the April 15 deadline.  

• $200 Early-bird Discount: This is automatically applied if you register before the April 15 early-bird deadline.  

• 10% Columbia Discount: This is valid for any active student, postdoc, staff, or faculty at Columbia University. If paying by credit card, use your Columbia email address during the registration process to automatically have the discount applied. If paying by internal transfer within Columbia, see below.  

• 10% Mailman Alumni Discount: This is valid for any individual who graduated from the Columbia University Mailman School of Public Health. To access the Mailman Alumni discount and receive a registration code, please email sharp_program@cumc.columbia.edu your graduation year and degree.  

• Group discounts are available for organizations sending 5+ participants. Please contact us directly at sharp_program@cumc.columbia.edu for more information.  

If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form (link to form coming soon) to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

If you would prefer to pay by invoice/check, please submit this Invoice Request form (link to form coming soon) to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

Cancellation notices must be received via email at least 30 days prior to the training start date in order to receive a full refund, minus a $75 administrative fee. Cancellation notices received via email 14-29 days prior to the training will receive a 75% refund, minus a $75 administrative fee. Please email your cancellation notice to Columbia.CMA@gmail.com. Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations less than 14 days prior to the training.  

If you are unable to attend the training, we encourage you to send a substitute within the same registration category. Please inform us of the substitute via email at least one week prior to the training so we can include them on attendee communications, gather registration details, and provide materials. Should the substitute fall within a different registration category (e.g., you are a faculty member and they are a postdoc), the credit card on file will be credited/charged respectively. Please email substitute inquiries to Columbia.CMA@gmail.com. In the event Columbia must cancel the event, your registration fee will be fully refunded.