2026/04/13 by Jennah Gosciak, Daniel Molitor, Ian Lundberg
Computer Science · Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Economic and Environmental Valuation #Mobile Crowdsensing and Crowdsourcing
paper · pdf · doi:10.1017/pan.2026.10040
openalex created_date 2025/10/10 · openalex publication_date 2026/04/13 · openalex updated_date 2026/08/01
Human choices are often both multi-dimensional and interactive. For example, a person deciding which of two immigrants is more worthy of admission to a country might weigh their education, and the weight placed on education may depend on other factors, such as their age, country of origin and employment history. We develop a response-adaptive experimental design that summarizes the range of effects of one attribute as a function of all other attributes. Our approach changes several aspects of the experimental design based on the ex ante choice to study the heterogeneous effects of one focal attribute (i.e., education). We update treatment assignment probabilities over the course of the experiment to search for the attribute vector at which the focal attribute has the most positive and most negative effects. By summarizing the full range of effects that exist, our approach complements existing approaches to conjoint experiments that typically aggregate over heterogeneity by marginalizing. We illustrate through two online experiments and provide customizable code infrastructure via a Docker container that other researchers can use to deploy adaptive randomization in online conjoint experiments.