  People can be characterized by their demographic information and personality
traits. Characterizing people accurately can help predict their preferences,
and aid recommendations and advertising. A growing number of studies infer
people's characteristics from behavioral data. However, context factors make
behavioral data noisy, making these data harder to use for predictive
analytics. In this paper, we demonstrate how to employ causal identification on
feature selection and how to predict individuals' characteristics based on
these selected features. We use visitors' choice data from a large theme park,
combined with personality measurements, to investigate the causal relationship
between visitors' characteristics and their choices in the park. We demonstrate
the benefit of feature selection based on causal identification in a supervised
prediction task for individual characteristics. Based on our evaluation, our
models that trained with features selected based on causal identification
outperformed existing methods.
