Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables

TL;DR

This paper introduces a Hilbert space embedding framework for qualitative data in causal models, improving estimation accuracy when dealing with dynamic, intricate, and sparse categorical information.

Contribution

It develops a novel functional analysis-based embedding method transforming categorical variables into a Reproducing Kernel Hilbert Space, enhancing causal estimation with complex qualitative data.

Findings

Superior performance in simulations and real-world e-commerce data

Effective handling of dynamic and sparse qualitative categories

Improved causal estimation accuracy

Abstract

We propose a novel framework for incorporating qualitative data into quantitative models for causal estimation. Previous methods use categorical variables derived from qualitative data to build quantitative models. However, this approach can lead to data-sparse categories and yield inconsistent (asymptotically biased) and imprecise (finite sample biased) estimates if the qualitative information is dynamic and intricate. We use functional analysis to create a more nuanced and flexible framework. We embed the observed categories into a latent Baire space and introduce a continuous linear map -- a Hilbert space embedding -- from the Baire space of categories to a Reproducing Kernel Hilbert Space (RKHS) of representation functions. Through the Riesz representation theorem, we establish that the canonical treatment of categorical variables in causal models can be transformed into an identified structure in the RKHS. Transfer learning acts as a catalyst to streamline estimation -- embeddings from traditional models are paired with the kernel trick to form the Hilbert space embedding. We validate our model through comprehensive simulation evidence and demonstrate its relevance in a real-world study that contrasts theoretical predictions from economics and psychology in an e-commerce marketplace. The results confirm the superior performance of our model, particularly in scenarios where qualitative information is nuanced and complex.