Mastering Field Experiments* - Online Course
A 4-Day Livestream Seminar Taught by
Jens HainmuellerTuesday, June 10 –
Friday, June 13, 2025
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
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 you 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.
Starting June 10, 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.
More details about the course content
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.
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.
Computing
The methods you will learn in this seminar can be applied in any software package. We will primarily walk through the analysis in R, but resources (e.g., replication code and examples) will also be provided for those who prefer to use Stata instead.
For R, you are strongly encouraged to use a computer with the most recent version of R installed. It is also recommended to download and install RStudio, a free front-end for R that makes it easier to work with.
For Stata, any version 14 or higher can be used to replicate the examples. If you are not ready to purchase Stata, you can take advantage of StataCorp’s 30-day software return policy.
The methods you will learn in this seminar can be applied in any software package. We will primarily walk through the analysis in R, but resources (e.g., replication code and examples) will also be provided for those who prefer to use Stata instead.
For R, you are strongly encouraged to use a computer with the most recent version of R installed. It is also recommended to download and install RStudio, a free front-end for R that makes it easier to work with.
For Stata, any version 14 or higher can be used to replicate the examples. If you are not ready to purchase Stata, you can take advantage of StataCorp’s 30-day software return policy.
Who should register?
This seminar is designed for those interested in gaining hands-on experience in field experiments, with a basic statistical background and some familiarity with regression techniques.
It is ideal for students, applied researchers in academia, industry, and government, as well as anyone looking to learn about designing and analyzing field experiments for the first time. This course is also valuable for experienced researchers who want to explore modern approaches to field experimentation.
A solid working knowledge of linear regression, at the level of textbooks like Wooldridge’s Introductory Econometrics or Lewis-Beck’s Applied Regression, is helpful.
This seminar is designed for those interested in gaining hands-on experience in field experiments, with a basic statistical background and some familiarity with regression techniques.
It is ideal for students, applied researchers in academia, industry, and government, as well as anyone looking to learn about designing and analyzing field experiments for the first time. This course is also valuable for experienced researchers who want to explore modern approaches to field experimentation.
A solid working knowledge of linear regression, at the level of textbooks like Wooldridge’s Introductory Econometrics or Lewis-Beck’s Applied Regression, is helpful.
Seminar outline
Foundations and design of field experiments
-
- Why field experiments matter
- The role of field experiments in establishing causal relationships in real-world settings
- Core concepts
- Key principles of randomization, control groups, treatment effects, and causal inference
- Randomization techniques
- Exploring methods such as simple randomization, stratification, and clustered randomization
- Estimating treatment effects
- Techniques for calculating average treatment effects and uncertainty estimates. Inference in small and large samples
Data analysis and practical applications
-
- Blocking strategies
- Enhancing balance and precision through blocking techniques
- Power calculations
- Determining optimal sample sizes for detecting treatment effects
- Addressing noncompliance
- Using local average treatment effects (LATE) with instrumental variables to account for noncompliance
- Handling missing data and attrition
- Strategies for managing incomplete data and participant attrition
Implementation and practical challenges
-
- Pre-analysis plans
- Structuring and registering pre-analysis plans to prevent p-hacking and selective reporting
- Ethical guidelines
- Addressing informed consent, participant welfare, and securing ethical approval for experiments
- Managing partnerships
- Building and maintaining successful collaborations with governments, NGOs, and other key stakeholders
Foundations and design of field experiments
-
- Why field experiments matter
- The role of field experiments in establishing causal relationships in real-world settings
- Core concepts
- Key principles of randomization, control groups, treatment effects, and causal inference
- Randomization techniques
- Exploring methods such as simple randomization, stratification, and clustered randomization
- Estimating treatment effects
- Techniques for calculating average treatment effects and uncertainty estimates. Inference in small and large samples
- Why field experiments matter
Data analysis and practical applications
-
- Blocking strategies
- Enhancing balance and precision through blocking techniques
- Power calculations
- Determining optimal sample sizes for detecting treatment effects
- Addressing noncompliance
- Using local average treatment effects (LATE) with instrumental variables to account for noncompliance
- Handling missing data and attrition
- Strategies for managing incomplete data and participant attrition
- Blocking strategies
Implementation and practical challenges
-
- Pre-analysis plans
- Structuring and registering pre-analysis plans to prevent p-hacking and selective reporting
- Ethical guidelines
- Addressing informed consent, participant welfare, and securing ethical approval for experiments
- Managing partnerships
- Building and maintaining successful collaborations with governments, NGOs, and other key stakeholders
- Pre-analysis plans
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.