Causal Mediation Analysis Training: Methods and Applications Using Health Data
August 28-30, 2023 | Subscribe to hear about the next training
The most recent Causal Mediation Analysis Training was on August 28 - 30, 2023. Sign up below to hear about the next training!
The Causal Mediation Analysis Training is a three-day intensive boot camp of seminars and hands-on analytical sessions to provide an overview of concepts and data analysis methods used to investigate mediating mechanisms.
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Summer 2023 dates: Livestream, online training August 28-30, 2023; 10:00am - ~5:00pm EDT
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, using ideas from causal inference and 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
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:
- Each participant must be familiar with linear and logistic regression.
- Each participant must have experience with programming in R.
- 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).
- Each participant is required to have a personal laptop/computer and a free, basic RStudio Cloud account. All lab sessions will be done using RStudio Cloud (now known as Posit).
Knowing basic R platform and commands is required for the training as noted in prerequisites above. This training will use RStudio Cloud (now Posit). 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 RStudio Cloud account for the training: Primers on Programming Basics and Visualization Basics.
- SHARP Program RStudio 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 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 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 eight 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 and Erasmus Universiteit Rotterdam (Netherlands).
Caleb Miles, PhD, Mailman School of Public Health, Columbia University. Caleb Miles is an Assistant Professor of Biostatistics at Columbia University Mailman School of Public Health. Dr. Miles works on developing semiparametric methods for mediation analysis and other branches of causal inference, and applying them to problems in public health. His applied work has largely been in HIV/AIDS, and he has more recently begun to work on psychiatric applications. He is currently working on methods to account for measurement error in mediation analysis, and developing hypothesis tests for mediated effects with improved power.
Training scholarships are available for the Causal Mediation Training.
Summer 2023: The Causal Mediation Analysis Training is a livestream, remote training that takes place over live, online video on August 28-30, 2023 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.
"This workshop gives an excellent, in-depth overview of causal mediation analysis. The instructors were well-prepared and clear. The hands-on labs were incredibly helpful for becoming familiar with new tools that we learned about during the workshop that I now plan to apply to my own research." - Faculty member at Dartmouth University 2022
"This is the excellent training course. The whole teaching and support team are knowledgeable and prepared." - Faculty Member, Tulane University, 2022
"Outstanding training. Wonderful, knowledgeable and patient instructors who explained complex concepts very well. " - Faculty Member, Icahn School of Medicine at Mt. Sinai, 2022
"This course provided a great opportunity to think through the key assumptions for implementing causal mediation analysis, and provided some tools (and resources for extending) for implementing these approaches in one's own work. I am leaving the course excited to apply these methods in two different ongoing projects and feel prepared to do so. " - Postdoctoral trainee, Emory University, 2022
This fee includes course material, which will be made available to all participants both during and after the conclusion of the training.
|Early-Bird Rate (through 6/15/23)||Regular Rate (6/16/22-8/18/23)||Columbia Discount*|
|Faculty/Academic Staff/Non-Profit Organizations/Government Agencies||$1,395||$1,595||10%|
*Columbia Discount: This discount 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, submit this Columbia Internal Transfer Request form 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.
Invoice Payment: If you would prefer to pay by invoice/check, please submit this Invoice Request form 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.
Cancellations: 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 <14days 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 to include them on attendee communications, updated registration forms, and materials. Should the substitute fall within a different registration category your credit card 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.
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The Causal Mediation Analysis Training is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.
"Fantastic course. Perfect pacing. The team makes extremely challenging concepts very clear, with the use of great examples and applied exercises." - Faculty member at the University of Alberta, 2021
"Excellent, concise workshop that brought together the essentials of causal mediation analysis and made it easy to learn, retain, and revisit the material. The live virtual format was ideal and well-organized, and the instructors were engaging and helpful throughout." - Postdoc at Harvard University, 2021
"Excellent and efficient review of causal mediation from very knowledgable instructors and direct hands on application of newly acquired knowledge. Highly recommended for those interested in expanding their skills or to get an introduction to the topic." - Faculty member at University of California, San Francisco, 2021
"This is an in-depth, incredibly useful causal mediation analysis that includes theory, code and practice. The content is technical yet appropriate for disciplinarily diverse audiences, is an excellent use of time." - Postdoc at the NIH, 2021
"The teachers verbalized the concepts very well and carefully phrased differences relative to traditional mediator analyses and conditioning on subsets in the data. These three days were time very well spent to get a grip on these approaches!" - Faculty member at UTHSC, 2021
Very organized materials and knowledgeable instructors. Efficient training course! - Faculty member at University of Pittsburgh, 2020
Absolutely amazing training! Very informative and perfectly organized even in the challenging pandemic times. A very comprehensive overview performed by the excellent instructors. Definitely worth to take part in and learn the power of mediation analyses. Thank you very much for organizing such a great event! - Magdalena K., Postdoc, 2020.
This is my first time attending a SHARP training and I had heard good things about these trainings. This far exceeded my expectations! This 3 day training was like taking a full month probably even two worth of work during a regular semesters course load. It is a very intense but enjoyable training due to the charismatic and very knowledgable lecturers and TA's. Linda and Caleb are amazing teachers of the art of Biostatistics and in this case causal mediation analysis. - Isela D., Student at the University of Texas Health Science Center School of Public Health, 2020.
This is an excellent crash course in how and why mediation approaches have changed over the last decade, with hands on learning of up-to-date skills. - Faculty member at Columbia University Irving Medical Center, 2020.
The training provided me with a good overview of causal mediation method from its concept and theory to implementation and potential applications. I am intrigued by this innovative method, have interests in learning more about it, and hopefully could apply it successfully to my current and future research projects. - Yuhong Z., Postdoc at the Medical College of Wisconsin, 2020.
Overall, this bootcamp provided me with the methods and tool to apply causal mediation analysis to my research. Based on this bootcamp, I feel confident that I can implement causal medication analyses and interpret findings correctly. - Alexi V., Student at the University of Washington, 2020.
This training provided really comprehensive instruction on causal mediation, which was enhanced by the hands-on lab. Overall, I felt like the gained the tools to implement causal mediation techniques in my own work. - Faculty member at University of Washington School of Public Health, 2019
Both instructors did a great job in pacing the material and explaining starting from the foundation. There were also some great examples that were being explored. - Kaylee H., Statistician at Weill Cornell Medicine, 2019
The format of the training with theory on Day 1, and practicum on day 2-3 afternoon was great; very useful for adult learning. The instructors were very patient with the wide range of skill levels of the trainees balancing the depth of teaching with the assistance in basic skills. - Vidhu T., Assistant Professor at Columbia University, 2019
The workshop was informative, the lecturers were knowledgeable and the hands-on component (lab) facilitated application of theoretical principles to techniques. - Lillian P, Postdoctoral research fellow at Columbia University, 2019
This was a highly useful, immediately applicable training led by excellent instructors. They provided an effective overview of underlying concepts (counterfactuals, directed acyclic graphs, marginal structural models), and in-depth instruction on various methods for assessing mediation, as well as opportunities to practice applying these methods using multiple statistical software programs. - Sarah L., Epidemiologist