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 4-Day Livestream Seminar Taught by Ashley Naimi, Ph.D

This course will focus on how to use experimental principles to appropriately design and analyze Monte Carlo simulation studies. Simulations are extremely flexible, and consequently are invaluable tools for understanding and applying a staggering range of methodologies. They are particularly useful for determining how methods will perform in the (all-too-typical) case when data analytic conditions differ from textbook-perfect ideals.

For example, simulations can be used to deepen understanding of often misunderstood concepts such as confidence intervals and hypothesis testing, to plan studies by comparing sampling strategies or running power analyses, to guide analyses by determining how well methods perform when their underlying assumptions are violated, and to assess robustness of results to threats ranging from unobserved confounding to choices about how data are coded and modeled.

This course will teach participants how to plan, conduct, and interpret simulation studies. Particular attention will be paid to key tasks including choosing an appropriate Monte Carlo sample size; managing computation time; applying a relevant data-generating mechanism using causal inference principles (via, e.g., DAGs); and efficiently analyzing simulated data. The course will conclude with a discussion of when more complex simulation designs are warranted, such as “plasmode” simulations or synthetic simulation (via variational autoencoders or generative adversarial networks).

Course concepts will be illustrated through an extended comparison of two average treatment effect estimators: inverse probability weighting and marginal standardization. After briefly reviewing how these estimators work, we will design a simulation study to evaluate their performance relative to one another. Throughout, we will use this example to emphasize the general skills needed to conduct simulation studies in a range of topic areas.