2025 CAUSALab Summer Courses on Causal Inference
Registration for CAUSALab’s Summer Courses on Causal Inference is open! Visit our portal to register now.
Low inventory: Advanced Confounding Adjustment (ACA) Online
We are excited to formally announce that CAUSALab is hosting its annual Summer Courses on Causal Inference between June 16 and June 27, 2025.
All summer courses will take place in Boston, Massachusetts at the Harvard T.H. Chan School of Public Health from 9:30 AM to 4:30 PM (ET) each day. Each course will offer a limited number of online seats for participants to attend virtually.
Participants will be able to register for one or two summer courses of their choice. Please note that courses offered during the same week occur simultaneously and cannot be taken at the same time.
Before registering, please review our refund policy, the differences between in-person and online experiences, and course dates and timing. All major credit cards and electronic checks are accepted for registration payment.
Each course is designed for a specific audience and has prerequisites that participants must meet in order to attend. To understand which courses are right for you, please read our descriptions below.
If you are interested in attending the 2025 courses, we recommend monitoring this page and CAUSALab social media channels regularly. Be the first to receive announcements by signing up for our 2025 courses listserv.
Prerequisite Courses
Fundamentals of Confounding Adjustment (FCA)
Online Prerequisite to Advanced Confounding Adjustment (ACA)
Causal inference from observational data often relies on appropriate adjustment for confounders. This online prerequisite course uses a combination of video lectures and hands-on exercises to introduce different methods to adjust for confounding in the context of time-fixed treatments. By the end of course, students will be able to:
- Explain why models are generally necessary to adjust for confounding
- Estimate causal effects of point interventions with adjustment for baseline confounding using various modeling approaches
- Understand the relative advantages and disadvantages of each modeling approach
Audience: FCA is designed for researchers and analysts who want to acquire skills to adjust for confounding in the time-fixed setting and the foundations for more advanced methods in the time-varying setting.
Instructors: Joy Shi, Barbra Dickerman, Miguel Hernán
Delivery: FCA is available March through mid-June 2025. Participants should complete FCA at their own pace prior to attending Advanced Confounding Adjustment (June 16-20, 2025).
Course Outline: learn more
Prerequisites: Participants are expected to have experience with the analysis of health databases in academic or industry settings, prior introductory courses on study design and data analysis, and working knowledge of R or SAS. Participants are expected to complete the following before the start of the course:
- Watch first three lectures of Causal Diagrams: Draw Your Assumptions Before Your Conclusions
- Read Part I of Causal Inference: What If (Hernán MA and Robins JM, Boca Raton: Chapman & Hall/CRC 2020)
Additional Information: $350 course tuition to be paid at the time of registration. This prerequisite course is non-degree and non-credit. Please note, no refunds will be offered once participant has access to the online course. Please refer to our Refund Policy for additional details.
Week One Courses (June 16-20, 2025)
No courses on June 19th in observance of Juneteenth. June 20th extended to full day.
Key Topics in Causal Inference (KTCI)
This 4-day course introduces concepts and methods for causal inference from observational data. Upon completion of the course, participants will be prepared to further explore the causal inference literature. Topics covered include the g-formula, inverse probability weighting of marginal structural models, causal mediation analysis and methods to handle unmeasured confounding. The last day will end with a “capstone” open Q&A session with the instructors.
Instructors: Miguel Hernán, Sara Lodi, Judith Lok, James Robins, Eric Tchetgen Tchetgen & Tyler VanderWeele
Prerequisites: Participants are expected to be familiar with basic concepts in epidemiology and biostatistics, including linear and logistic regression and survival analysis techniques.
Audience: Researchers interested in acquiring a roadmap to navigate the literature on causal inference methods.
Advanced Confounding Adjustment (ACA)
Causal inference from observational data often relies on appropriate adjustment for confounders. In this 4-day course, students will learn how to implement advanced g-methods for confounding adjustment—inverse probability weighting and the parametric g-formula—in increasingly complex analytical settings. The course discusses these methods in the time-varying setting using a combination of lectures and hands-on sessions. All hands-on sessions will offer a choice between R and SAS.
Instructors: Barbra Dickerman, Joy Shi, Miguel Hernán
Course Outline: learn more
Prerequisites: Starting this year, participants must (i) complete the online prerequisite course, Fundamentals of Confounding Adjustment (FCA), before attending this course, or (ii) have equivalent prior knowledge of methods to adjust for confounding in the time-fixed setting, including but not limited to inverse probability weighting and standardization/g-formula. Participants are expected to have experience with the analysis of health databases in academic or industry settings. Prior introductory coursework on study design and data analysis, working knowledge of R or SAS and a laptop computer with R or SAS is required.
Audience: Researchers and analysts who want to acquire advanced skills that are required for causal inference with time-varying treatments.
Week Two Courses (June 23-27, 2025)
Combining Information for Causal Inference (CICI)
This 5-day course will provide hands-on training for causal inference by combining information from multiple and diverse sources. Students will learn concepts and methods for:
- Generalizability & transportability analyses that extend causal inferences from one or more randomized trials to a new target population
- External comparisons between an intervention examined in a single-group or comparative experimental study versus other interventions not examined in the experimental study
- Indirect comparisons of different experimental treatments evaluated in separate trials against a common control treatment
- General study design by leveraging novel matching and weighting methods that directly balance covariates
Emphasis will be on implementing the methods for causal research with randomized trial and real-world data. Through a combination of lectures and hands-on sessions, the course will introduce different settings for combining information and examine different examples of combining information in the health sciences. Students will learn how to carry out causal analyses using data from trials and healthcare databases (e.g., administrative claims and electronic health records). R will be the main programming language for the course and used across all sections.
Instructors: Issa Dahabreh, José Zubizarreta
Prerequisites: Participants are expected to have experience with the analysis of health data in academic or industry settings. Prior introductory courses on study design and data analysis, working knowledge of R or SAS* and a laptop computer with R or SAS*.
*Examples in both R and SAS programming languages will be presented in all but two sessions, where only R will be used. We recommend that SAS users taking this course prepare by familiarizing themselves with R ahead of these sessions.
Audience: Researchers and analysts who want to use combinations of randomized trial and real-world data to learn what works.
Target Trial Emulation (TTE)
This 5-day course will provide hands-on training for causal inference using health databases. Students will learn the principles of target trial emulation and how to implement them for causal research with real-world data. Causal inference from observational data can be conceptualized as an attempt to emulate a pragmatic randomized trial—the target trial. Through a combination of lectures and hands-on sessions, the course introduces the target trial emulation framework in increasingly complex settings and dissects examples of emulations in the health sciences and related fields. Students will learn how to design target trial emulations and carry out appropriate causal analyses of healthcare databases such as administrative claims and electronic health records. All sessions will offer a choice between R and SAS.
Instructors: Barbra Dickerman, Joy Shi, Miguel Hernán
Course Outline: learn more
Prerequisites: Participants are expected to have experience with the analysis of health databases in academic or industry settings. Prior introductory coursework on study design and data analysis, working knowledge of R or SAS and a laptop computer with R or SAS is required. Participants must also either (i) complete the CAUSALab’s Advanced Confounding Adjustment course, or (ii) have equivalent prior knowledge of methods to adjust for confounding in the time-varying setting including inverse-probability weighting.
Audience: Researchers and analysts who use real-world databases to learn what works.
Attend the Courses
In-Person Option
Location: Harvard T.H. Chan School of Public Health (677 Huntington Avenue, Boston, MA 02115)
2025 Tuition Prices:
$1,400.00 – Week 1 – Key Topics in Causal Inference
$1,800.00 – Week 1 – Advanced Confounding Adjustment
$1,800.00 – Week 2 – Combining Information for Causal Inference
$1,800.00 – Week 2 – Target Trial Emulation
These courses are non-degree and non-credit. Enrollment in either course is not eligible for visa sponsorship. Course tuition does not cover housing or transportation costs to the courses.
Online Alternative
2025 Tuition Price:
$800 per course (KTCI, ACA, CICI, TTE) to be paid at the time of registration. These courses are non-degree and non-credit.
Online participants will be granted access to the same materials and schedule as those who attend the courses in person. Participants are expected to attend the live sessions as no recordings will be allowed or available if missed.
Please note that online participants will not be able to do the following: attend hands-on sessions or Q&As, attend in-person networking events, ask questions, or receive a certificate of completion. If you are interested in receiving the full experience of each course, we strongly recommend that you register to attend in person.
Enrollment for online courses is limited. Once registration opens, admission is granted on a first come, first served basis with a capped number of available slots. If you are interested in attending a course virtually, we recommend registering shortly after registration opens.
Refund Policy
Participants have 48 hours from their registration confirmation to cancel their order and receive a full tuition refund. After 48 hours, no refunds will be allowed. For online courses, CAUSALab and Harvard University are not liable for any technical problems or system failures on a user’s end. Participants attending the online courses are responsible for their own access to a digital device and reliable internet connection.
Student Tuition Waivers
No waivers are offered for the online alternative or prerequisite course. Students cannot apply for all four courses. Those who choose to apply for two courses instead of one must choose courses that do not run during the same week.
The application portal closed on February 15, 2025. Decisions will be sent March 2025.
| 1) Hold an active PhD student status at the time of the courses (students graduating May 2025 do not qualify) |
| 2) Be eligible to enter the U.S. and cover travel expenses to attend the courses in Boston, MA |
| 3) Have no alternative funding sources (e.g., NIH Funding) |
| 4) Have not received a tuition waiver for a previous CAUSALab course |
Frequently Asked Questions (FAQ)
No, housing is not provided by the university for those enrolled. Please see below for recommendations and information about the area.
The CAUSALab summer courses will be held at Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA. This area is known as the Longwood Medical Area. The Harvard Longwood Campus is home to Harvard T.H. Chan School of Public Health, Harvard Medical School and Harvard School of Dental Medicine, and is located 4.5 miles from Harvard Square in Cambridge. There are many housing options just a short public transit or taxi ride away.
Participants are responsible for arranging and paying for their own accommodations. Please contact the hotels directly for rates, availability, and booking.
All major credit cards and electronic checks are accepted for registration payment. Upon registration, an order confirmation will be sent via email with billing & payment information.
MBTA Subway “T” – The Green Line, Branch D, runs between Riverside (in Newton) and Government Center (in downtown Boston). The Longwood stop is a 15 minute walk from HSPH. The Green Line, Branch E, runs between Heath Street (Mission Hill/Jamaica Plain) and Lechmere (E. Cambridge). The Brigham Circle stop is across the street from the Huntington Avenue entrance of the Harvard T.H. Chan School of Public Health.
MBTA Bus – The MBTA also offers an extensive bus system, which has stops all around the Longwood Medical Area, Mission Hill, and the rest of the Boston area. Be sure to check out the MBTA Trip Planner to find the best route to HSPH and around town for you.
Car – Unfortunately, Harvard does not offer parking options for those who wish to drive to attend the courses. We recommend that participants take advantage of the public transportation and ride share options whenever possible as parking can be expensive in the city.
Parking – If you are driving and will need a place to park, look at public parking garages in the area. Please check the rates in advance to determine what you are comfortable paying. Parking garages near by include, but are not limited to:
Simmons University Parking Garage (10 Min Walk)
333 Longwood Ave Garage (10 Min Walk)
375 Longwood Ave Garage (12 Min Walk) – Is slightly cheaper
Ride Share Options – Uber and Lyft are two very popular and easy methods of ride share transportations in the city. These services are accessible through their mobile applications which you can download for free. Taxis can now also be called through the Uber app.
Our History
In 2017, Miguel Hernán, Judith Lok, James Robins, Eric Tchetgen Tchetgen and Tyler VanderWeele set off to create a public, introductory course on causal inference at Harvard University. This course is still offered today under the name “Key Topics in Causal Inference.”
In 2021, CAUSALab was founded under the direction of Miguel Hernán with a core mission of providing training on causal inference to the next generation of investigators.
Building upon this foundation, 2022 saw the introduction of the Target Trial Emulation course, accompanied by the official establishment of the CAUSALab Summer Courses on Causal Inference.
In 2023, four courses were offered over two weeks for individuals to attend in person or virtually for the first time. A total of 445 participants attended representing over 30 countries and 60+ different organizations that spanned across academia and industry. Additionally, 43 tuition waivers were awarded to 28 students.
The 2024 summer courses cohort included 381 participants from 35+ countries and 150+ organizations. 42 fee waivers were awarded across all four course offerings.
Questions?
Inquiries can be directed to CAUSALab.