Causal Mediation Analysis - Online Course
A 4-Day Livestream Seminar Taught by
Linda Valeri10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
Causal mediation analysis offers a sophisticated method to understand the mechanisms through which an intervention or treatment exerts its effects on an outcome. This type of analysis is crucial for identifying the indirect effects mediated through specific variables, allowing researchers to dissect the causal pathways and potentially target these mechanisms more effectively in future interventions.
Causal mediation analysis extends beyond conventional mediation analysis by providing a framework to evaluate potential causal roles of mediators. It enables researchers to estimate both direct effects of a treatment or exposure on an outcome and indirect effects that operate through one or more mediators. This is particularly important in fields like public health, education, and social sciences, where understanding the underlying processes can lead to more effective interventions and policies.
This seminar will focus on some of the recent developments in causal mediation analysis and will provide practical tools to implement those techniques. We will discuss the relationship between traditional methods for mediation in the biomedical and social sciences and new methods of causal inference for dichotomous, continuous, and time-to-event outcomes.
The course takes place online in the span of 4 days with live instruction morning (10:30-12:30) sessions and lab activities in afternoon sessions (1:30-3:00) when you will apply what you’ve learned to a data set.
Starting May 19, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
We will approach concepts and methods for mediation from the perspective of the counterfactual framework. Definitions, theoretical identification results, and statistical techniques related to mediation analysis will be covered.
In the first part, we will clarify the no-confounding assumptions required for the estimation of direct and indirect effects and will also consider when standard approaches to mediation analysis are valid and when they are not valid. These approaches will be extended to more complex settings, such as in the presence of interactions, non-linearities, and time-varying exposures.
The second part will cover selected advanced topics: multiple mediators, multiple exposures, time-to-event outcomes.
Upon successful completion of this course, you should be able to:
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- Explain when traditional methods for mediation fail.
- Define the concepts about mediation from causal inference.
- Conduct regression methods for mediation with single mediators and time-to-event outcomes and interpret results of such analyses.
- Discuss strategies to conduct mediation analysis with multiple mediators and multiple exposures.
We will approach concepts and methods for mediation from the perspective of the counterfactual framework. Definitions, theoretical identification results, and statistical techniques related to mediation analysis will be covered.
In the first part, we will clarify the no-confounding assumptions required for the estimation of direct and indirect effects and will also consider when standard approaches to mediation analysis are valid and when they are not valid. These approaches will be extended to more complex settings, such as in the presence of interactions, non-linearities, and time-varying exposures.
The second part will cover selected advanced topics: multiple mediators, multiple exposures, time-to-event outcomes.
Upon successful completion of this course, you should be able to:
-
- Explain when traditional methods for mediation fail.
- Define the concepts about mediation from causal inference.
- Conduct regression methods for mediation with single mediators and time-to-event outcomes and interpret results of such analyses.
- Discuss strategies to conduct mediation analysis with multiple mediators and multiple exposures.
Computing
R will be used for all demonstrations. SAS and Stata code will be made available but will not be used in the live instruction.
R packages, SAS, and Stata macros to implement mediation methods will be distributed to course participants. Implementation of mediation analysis approaches under the counterfactual framework will be illustrated in hands-on laboratories.
Familiarity with R, SAS, or Stata will be necessary to follow laboratory activities.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
R will be used for all demonstrations. SAS and Stata code will be made available but will not be used in the live instruction.
R packages, SAS, and Stata macros to implement mediation methods will be distributed to course participants. Implementation of mediation analysis approaches under the counterfactual framework will be illustrated in hands-on laboratories.
Familiarity with R, SAS, or Stata will be necessary to follow laboratory activities.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
Who should register?
The course will be especially valuable for those interested in understanding the theoretical underpinnings of causal mediation analysis as well as in practicing the implementation and interpretation of state-of-the-art causal inference approaches for reproducible causal mediation analyses.
Familiarity with counterfactuals and linear and logistic regression will be assumed. Some exposure to inverse probability of treatment weighting, marginal structural models, and causal diagrams will be helpful.
The course will be especially valuable for those interested in understanding the theoretical underpinnings of causal mediation analysis as well as in practicing the implementation and interpretation of state-of-the-art causal inference approaches for reproducible causal mediation analyses.
Familiarity with counterfactuals and linear and logistic regression will be assumed. Some exposure to inverse probability of treatment weighting, marginal structural models, and causal diagrams will be helpful.
Seminar outline
DAY 1: Mediation – traditional approaches and concepts under the counterfactual framework
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- Introduction to questions of mechanism
- Product and difference method approaches for mediation: theory and case study application
- Direct and indirect effects under the counterfactual framework for causal inference
- No-confounding assumptions required for the estimation of direct and indirect effects: theory and application using directed acyclic graphs
DAY 2: Regression methods for direct and indirect effects
-
- Parametric regression approaches for mediation analysis with continuous outcomes
- Parametric regression approaches for mediation analysis with binary outcomes
- Approaches for case-control design
- Overview of software for causal mediation analysis and case study application
DAY 3: Mediation analysis with multiple mediators and multiple exposures
-
- Parametric and weighting based regression approaches for mediation analysis with multiple mediators
- Path-specific effects and randomized interventional analogues to direct and indirect effects in the presence of multiple mediators and time-dependent confounding
- Bayesian machine learning approaches for mediation analysis with multiple exposures
- Discuss potential violation of mediation analysis assumptions and choose appropriate strategies to address such violations in a case study application
DAY 4: Mediation analysis with time-to-event outcomes
-
- Definition of direct and indirect effects with time-to-event outcomes
- Parametric and semi-parametric regression approaches for mediation analysis with time-to-event outcome
- Semi-parametric regression approaches for mediation analysis with time-to-event outcome and time-to-event mediator in the presence of competing risks
- Case study application of mediation analysis with time-to-event outcomes
DAY 1: Mediation – traditional approaches and concepts under the counterfactual framework
-
- Introduction to questions of mechanism
- Product and difference method approaches for mediation: theory and case study application
- Direct and indirect effects under the counterfactual framework for causal inference
- No-confounding assumptions required for the estimation of direct and indirect effects: theory and application using directed acyclic graphs
DAY 2: Regression methods for direct and indirect effects
-
- Parametric regression approaches for mediation analysis with continuous outcomes
- Parametric regression approaches for mediation analysis with binary outcomes
- Approaches for case-control design
- Overview of software for causal mediation analysis and case study application
DAY 3: Mediation analysis with multiple mediators and multiple exposures
-
- Parametric and weighting based regression approaches for mediation analysis with multiple mediators
- Path-specific effects and randomized interventional analogues to direct and indirect effects in the presence of multiple mediators and time-dependent confounding
- Bayesian machine learning approaches for mediation analysis with multiple exposures
- Discuss potential violation of mediation analysis assumptions and choose appropriate strategies to address such violations in a case study application
DAY 4: Mediation analysis with time-to-event outcomes
-
- Definition of direct and indirect effects with time-to-event outcomes
- Parametric and semi-parametric regression approaches for mediation analysis with time-to-event outcome
- Semi-parametric regression approaches for mediation analysis with time-to-event outcome and time-to-event mediator in the presence of competing risks
- Case study application of mediation analysis with time-to-event outcomes
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.