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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 Jens Hainmueller, Ph.D.

A Practical Guide to Design, Analysis, and Implementation

Field experiments are the gold standard for understanding causal relationships in real-world settings and are widely applied across the social sciences, public policy, and management. This course offers a comprehensive and practical guide to designing, implementing, analyzing, and managing field experiments. It equips participants with the technical skills and managerial expertise necessary to successfully conduct field experiments across various applied settings.

By the end of the course, you will have the knowledge and skills to independently design, implement, and analyze field experiments, including pre-analysis planning, while effectively managing partnerships and ethical considerations. You will be prepared to tackle complex causal questions in diverse applied settings.

The course begins by introducing why field experiments matter, emphasizing their role in establishing causal relationships within real-world contexts. You will learn the core concepts of field experiments, including principles of randomization, control groups, treatment effects, and causal inference.

We will then explore randomization techniques, such as simple randomization, stratification, and clustered randomization, followed by methods for estimating treatment effects, including calculating average treatment effects and uncertainty estimates in both small and large samples.

Next, the course delves into practical data analysis applications. You will learn about blocking strategies to enhance balance and precision in experiments, as well as power calculations to determine optimal sample sizes for detecting treatment effects. Additionally, we’ll cover addressing noncompliance by applying local average treatment effects (LATE) using instrumental variables, as well as handling missing data and attrition to manage incomplete data and participant drop-off effectively.

Then we will focus on implementation and overcoming practical challenges. We will develop pre-analysis plans, understanding how to structure and register them to ensure transparency and prevent selective reporting or p-hacking. We will also cover ethical guidelines, addressing informed consent, participant welfare, securing ethical approvals for experiments, and highlight managing partnerships with governments, NGOs, and other stakeholders.

Core technical concepts will be illustrated using real datasets and code, allowing you to gain hands-on experience with practical examples. This approach ensures that you not only grasp the theoretical principles but also develop the practical skills to apply them effectively.