|
View: |
Part 1: Document Description
|
|
Citation |
|
|---|---|
|
Title: |
Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis |
|
Identification Number: |
doi:10.7910/DVN/ENI8GF |
|
Distributor: |
Harvard Dataverse |
|
Date of Distribution: |
2023-11-29 |
|
Version: |
1 |
|
Bibliographic Citation: |
Ham, Dae Woong; Kosuke Imai; Lucas Janson, 2023, "Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis", https://doi.org/10.7910/DVN/ENI8GF, Harvard Dataverse, V1 |
|
Citation |
|
|
Title: |
Replication Data for: Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis |
|
Identification Number: |
doi:10.7910/DVN/ENI8GF |
|
Authoring Entity: |
Ham, Dae Woong (Harvard University) |
|
Kosuke Imai (Harvard University) |
|
|
Lucas Janson (Harvard University) |
|
|
Producer: |
<i>Political Analysis</i> |
|
Distributor: |
Harvard Dataverse |
|
Access Authority: |
Ham, Dae Woong |
|
Depositor: |
Ham, Dae Woong |
|
Date of Deposit: |
2023-04-08 |
|
Holdings Information: |
https://doi.org/10.7910/DVN/ENI8GF |
|
Study Scope |
|
|
Keywords: |
Social Sciences |
|
Abstract: |
Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Many researchers focus on estimating the average marginal effects of each factor while averaging over the other factors. Although this allows for straightforward design-based estimation, the results critically depend on the ways in which factors interact with one another. An alternative model-based approach can compute various quantities of interest, but requires correct model specification, a challenging task for conjoint analysis with many factors. We propose a new hypothesis testing approach based on the conditional randomization test to answer the most fundamental question of conjoint analysis: Does a factor of interest matter in any way given the other factors? Although it only provides a formal test of these binary questions, the CRT is solely based on the randomization of factors, and hence requires no modeling assumption. This means that the CRT can provide a powerful and assumption-free statistical test by enabling the use of any test statistic, including those based on complex machine learning algorithms. We also show how to test commonly used regularity assumptions. Finally, we apply the proposed methodology to conjoint analysis of immigration preferences. An open-source software package is available for implementing the proposed methodology. |
|
Methodology and Processing |
|
|
Sources Statement |
|
|
Data Access |
|
|
Other Study Description Materials |
|
|
Related Publications |
|
|
Citation |
|
|
Title: |
Forthcoming, Political Analysis |
|
Bibliographic Citation: |
Forthcoming, Political Analysis |
|
Label: |
CRT_Replication_Final.zip |
|
Notes: |
application/zip |