A Practitioner's Guide to Treatment Effects in Experimentation: ATE, CATE, ITT, LATE & ATT Explained | Growthbook Blog
Blog post from GrowthBook
In the context of experimentation and treatment effects, the text explores the complexities of interpreting different metrics that result from randomized trials, emphasizing the importance of distinguishing between various treatment effects, such as Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE), Intention-to-Treat effect (ITT), Local Average Treatment Effect (LATE), and Average Treatment Effect on the Treated (ATT). Using a food delivery platform's free trial experiment as an example, the text illustrates how different analytical frameworks and assumptions can lead to varying interpretations of the same data, highlighting the potential for selection bias when comparing subgroups without proper randomization. By employing concepts from causal inference, the text underscores the necessity of understanding which metric accurately answers the business question at hand, and why randomization is crucial for isolating the true effect of an intervention. It also explains how different treatment effects provide insights into customer behavior, with LATE focusing on those who comply due to the assignment, and emphasizes that the correct interpretation of these effects is vital for making informed business decisions.
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