Embedding Categorical Variables to Improve Causal Inference in High-Dimensional and Sparse Data
Categorical variables with high cardinality and sparsity pose challenges for causal inference. The authors propose a method called CAVIAR that embeds categorical variables into a lower-dimensional space to enable stable and robust estimates through dimensionality reduction.