Problem
Research questions and friction points this paper is trying to address.
effect size
individual variation
average treatment effect
experimental design
observational study
Hypothesizing an effect size by considering individual variation
🤖 AI Summary
This study addresses the common reliance on unrealistic assumptions about average treatment effects in experimental and observational research designs. It proposes a novel paradigm that shifts focus from directly positing average effects to modeling the full distribution of individual treatment effects, from which more plausible assumptions about average effects can be derived. By integrating distributional modeling with cross-disciplinary case studies, the approach demonstrates its validity and utility across diverse fields—including medicine, economics, and psychology—offering researchers a principled, heterogeneity-aware framework for specifying effect sizes grounded in empirical realism rather than idealized assumptions.
📝 Abstract
When designing and evaluating an experiment or observational study, it is useful to have a realistic hypothesis regarding the average treatment effect. We present an approach to conceptualizing this average by first considering a distribution of effects. We demonstrate with examples in medicine, economics, and psychology.
Innovation
Methods, ideas, or system contributions that make the work stand out.
effect size
individual variation
average treatment effect
heterogeneous effects
study design
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