Hi all,
Our next virtual meeting will be at 12pm (EST) Wednesday, April 7
(tomorrow), where David Ham (Harvard) will present "Using Machine Learning
to Test Hypothesis in Conjoint Analysis." This is joint work with Lucas
Janson and Kosuke Imai.
*Abstract*:
Conjoint analysis is a popular experimental design used to measure
multidimensional preferences. Researchers examine how varying a factor of
interest, while controlling for other relevant factors, impacts
decision-making. Currently, there exist two methodological approaches to
analyzing data from a conjoint experiment. The first focuses on estimating
marginal effects of each factor while averaging over the other factors.
Although this allows for straightforward nonparametric estimation using a
design-based approach, the results critically depend on the distribution of
other factors and how interaction effects are aggregated. An alternative
approach is model-based and in principle can compute any quantities of
interest. The primary drawback is that researchers must correctly specify
the model, a challenging task for conjoint analysis with many factors. In
addition, a commonly used logistic regression has poor statistical
properties even with a moderate number of factors. We propose a new
hypothesis testing approach based on the conditional randomization test.
We answer the most fundamental question of conjoint analysis: Does a factor
of interest matter in any way given the other factors? Our methodology is
solely based on the randomization of factors, and hence is free from
assumptions. Yet, it allows researchers to use any test statistic,
including those based on complex machine learning models. As a result, we
are able to combine the strengths of the existing design-based and
model-based approaches. We illustrate the proposed methodology through
conjoint analysis of immigration preferences. An open-source software
package is available for implementing the proposed methodology.
*Zoom link*:
https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all tomorrow!
Best,
Soichiro
--
Soichiro Yamauchi
PhD candidate
Harvard University
URL:
https://soichiroy.github.io/