This package allows researchers to estimate the causal effects of attributes in conjoint survey experiments. It implements the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30
Generates a "conjointDesign" object to be passed to amce. Average Marginal Component Effects (AMCEs) are defined relative to the distribution of potential choice profiles. When the probability of each profile being presented to a respondent is not constant (e.g. some profiles are restricted from appearing), some attributes will not be independent and simple difference-in-means estimates will not be unbiased for the AMCE. This function allows users to specify non-uniform profile assignment schemes to be used by amce.
This function takes a dataset and a conjoint design and returns Average Marginal Component Effects (AMCEs) and Average Component Interaction Effects (ACIE) for the attributes specified in the formula. By default, this function assumes uniform randomization of attribute levels and no profile restrictions. If your design incorporates weighted randomization or restrictions on displayable profiles, first generate a design object using makeDesign. Interactions with respondent-level characteristics are handled by identifying relevant variables as respondent-varying.
Converts the raw .CSV data file downloaded from an online conjoint experiment run using the Qualtrics survey software into a data frame usable by the amce routine. Each row of the Qualtrics .CSV file is a single survey respondent. The rows of the resulting dataframe correspond to individual profile choices. Currently, this function only works for designs with a binary outcome variable (selected/not selected) that requires a single choice among profiles.
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