Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A Distinguished Speaker Series Seminar by Tyler VanderWeele, Ph.D.

This seminar will provide an overview of the principles of causal inference and the extension of those principles to outcome-wide studies. Outcome-wide studies broaden traditional approaches to assessing the causal effects of an exposure using confounding control, but over numerous outcomes rather than a single outcome. Methodological topics addressed in the workshop will include:

• Temporal and confounding control principles for causal inference.
• Sensitivity analysis metrics, such as the E-value, to evaluate robustness or sensitivity of effect estimates to potential unmeasured confounding.
• Approaches to handle multiple testing.

We will also discuss some of the advantages of outcome-wide designs over more traditional studies of single exposure-outcome relationships including:

• Results that are less subject to investigator bias.
• Greater potential to report null effects.
• Greater capacity to compare effect sizes.
• A tremendous gain in the efficiency for the research community.
• A greater policy relevance.
• A more rapid advancement of knowledge.

We’ll discuss both the practical and theoretical justification for outcome-wide longitudinal studies, as well as the pragmatic details and limitations of their implementation. Outcome-wide designs, incorporating the principles of causal inference, have the potential to more rapidly advance our knowledge within the social and biomedical sciences.

Prerequisites: Knowledge of linear and logistic regression. Some familiarity with principles of causal inference is desirable but not required.