Registration is open! Join us for the next live-stream Causal Mediation Analysis Training on August 17-19, 2022.
The Causal Mediation Analysis Training is a 3-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.
Summer 2022 dates: Live-stream, online training August 17-19, 2022; 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 at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
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.
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.
COVID-19 Update: The Causal Mediation Analysis Training will not take place in person due to the COVID-19 pandemic. Instead, the Training will be a live-stream, remote training that takes place over live, online video on August 17-19, 2022 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.
"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
COVID-19 Update: With the training being offered virtually instead of in-person, we are passing along any and all costs saved to attendees.
|Early-Bird Rate (through 6/15/22)||Regular Rate (6/16/22-8/10/22)||Columbia Discount*|
|Faculty/Academic Staff/Non-Profit Organizations/Government Agencies||$1,375||$1,575||10%|
*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. To access Columbia discount, email Columbia.CMA@gmail.com for instructions and specify the following: 1) your registration category from the table above, 2) if you are paying by credit card, or internal transfer within Columbia, and 3) if an internal transfer, indicate the registration category from the table above, and if grant funds are being used..
Invoice Payment and Group Registrations: If you would prefer to pay by invoice/check, or would like to pay for a group of registrants, please email Columbia.CMA@gmail.com with the following details: 1) full attendee name(s) and applicable registration category from the table above, and 2) payment method (credit card, invoice, wire).
Registration Fee: This fee includes course material, which will be made available to all participants both during and after the conclusion of the training.
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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Questions? Email the CMA team at Columbia.CMA@gmail.com.
The Causal Mediation Analysis Training is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.
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