Assessing methods for generalizing experimental impact estimates to target populations.

Area of Science:

• Causal inference
• Statistical modeling
• Education research

Background:

• Randomized experiments are the gold standard for estimating treatment effects.
• Generalizing findings from experiments to broader target populations is a key challenge.
• Experimental participants may not be representative of the population of interest.

Purpose of the Study:

• To examine statistical methods that can assist in generalizing findings from randomized experiments to target populations.
• To compare the performance of different statistical adjustment methods.

Main Methods:

• The study examines reweighting and outcome modeling approaches.
• Two simulation studies (one with simulated data, one with dropout prevention program data) were conducted.
• An empirical analysis using multi-site experiment data was performed.

Main Results:

• Machine learning methods outperformed regression-based methods when ignorability assumptions were met.
• All methods performed poorly when structural assumptions were violated.
• Flexible modeling approaches showed the best performance in the empirical analysis, outperforming linear regression.

Conclusions:

• Flexible statistical modeling techniques can aid in generalizing findings from randomized experiments.
• The effectiveness of these generalization methods relies on strong, often untestable, assumptions.
• Even advanced statistical techniques require careful consideration of their underlying assumptions.