2024 Causal Courses

CAUSALab Courses PosterWe are excited to formally announce that CAUSALab is hosting its annual summer of courses on causal inference between June 3 and June 14, 2024. Registration opens Wednesday, February 7, 2024, beginning at 12:00 PM ET.

All 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 starting in early February 2024 for one or two courses of their choice. Please note that courses offered during the same week occur simultaneously and cannot be taken at the same time.

Each course is designed for a specific audience and has various prerequisites that participants must meet in order to attend. To understand which courses are right for you, please read our descriptions below.


Register HereCourse Registration is Open!

Before registering, please take a moment to review our refund policy, the differences between in-person and online experiences, as well as course dates and timing. All major credit cards and electronic checks are accepted for payment.

Due to high demand, certain courses may reach capacity quickly. Enrollment operates on a first come first serve basis. We recommend participants register as soon as possible to ensure they can attend a course of their choosing. If you are interested in attending and would like to receive course announcements, sign up for our listserv.


Registration Updates

The following online courses are currently at maximum capacity. If you are interested in attending one of these virtual courses please join the waitlist. If a seat becomes available we will reach out to you.

  • Advanced Confounding Adjustment (Online) – FULL
  • Target Trial Emulation (Online) – FULL

Updated as of 03.15.2024


Week One: Key Topics in Causal Inference

This 5-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 LokJames RobinsEric 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.

Week One: Advanced Confounding Adjustment

Causal inference from observational data often relies on appropriate adjustment for confounders. In this 5-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 introduces these methods in the context of a time-fixed treatment and extends them to 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: Joy ShiBarbra DickermanMiguel Hernán    Curriculum Fellow: Sophia Rein

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.

Audience: Researchers and analysts who want to acquire skills that are required for causal inference with time-varying treatments.

Week Two: Combining Information for Causal Inference

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 DahabrehJosé Zubizarreta    Curriculum Fellows: Sarah Robertson, Lucy Shen

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.

Week Two: Target Trial Emulation

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   Curriculum Fellow: Emma McGee

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.


In-Person Option

Location: Harvard T.H. Chan School of Public Health
677 Huntington Avenue, Boston, MA 02115

2024 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 – Target Trial Emulation
$1,800.00 – Week 2 – Combining Information for Causal Inference

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

Individuals unable to be physically present in Boston but interested in attending a course should explore our online alternative. Through this option, participants will have the the opportunity to attend course lectures virtually through Zoom and access course materials.

2024 Tuition Price: $800 per course 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 is open, admission is granted on a first come first served basis with a restricted number of available slots. If you are interested in attending a course virtually, we recommend that you register as soon as possible.


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

A limited number of tuition waivers are available for qualified students to attend the courses in person. All students must apply for tuition waivers for one or two courses of their choice by submitting a CV and a brief statement of interest (500 word limit) by Sunday, February 18th at 11:59 PM (EST). Statement of interests should include plans for future application and show that students are well equipped to attend based on their existing knowledge and experience. Decisions will be emailed to applicants in March 2024.

No waivers are offered for the online alternative. Students cannot apply for all four courses. Those who choose to apply for two courses instead of one must choose courses that are not run during the same week.

IMPORTANT: Before applying, make sure that you are eligible to receive a student waiver based on the criteria below. Applicants must meet all criteria to be considered, no exceptions.

1) Active master’s or PhD student status at the time of the courses (students graduating in May of 2024 do not qualify)
2) Eligible to enter the United States and cover traveling expenses of Boston, Massachusetts
3) No alternative funding sources (e.g., NIH Funding)
4) Has not received a tuition waiver for a previous CAUSALab courses

Our submission portal is now closed and applications are being reviewed for the 2024 courses.


Common FAQs


Our History

Logo collage of the various universities and companies of the 2023 participants
Logo collage of the various universities and companies of the 2023 participants

In 2017, Miguel Hernán, Judith Lok, James Robins, Eric Tchetgen Tchetgen and Tyler VanderWeele set off to teach an introduction course on causal inference open to the public at Harvard University. This course is still offered today under the name “Key Topics in Causal Inference.”

In 2021, The 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.


Additional questions?
Inquiries can be directed to The CAUSALab.