Type of covariate/predictor variable for conversion analysis. Different types enable different analysis strategies: - CATEGORICAL: String values, analyzed via grouping (e.g., sender_domain, priority) - NUMERIC: Continuous values, binned into quartiles/deciles (e.g., word_count, price) - EMBEDDING: Dense vectors, clustered for semantic analysis (e.g., CLIP embeddings) - CLUSTER_ID: Pre-computed cluster identifiers (e.g., topic_cluster, visual_cluster) Examples: python # Categorical: Which email domains convert better? CovariateConfig(field_path=\"sender_domain\", covariate_type=\"categorical\") # Numeric: Do longer emails convert faster? CovariateConfig(field_path=\"word_count\", covariate_type=\"numeric\") # Embedding: Do visually similar images follow similar paths? CovariateConfig(field_path=\"features.clip\", covariate_type=\"embedding\") # Cluster: Which topic clusters have highest conversion? CovariateConfig(field_path=\"metadata.topic_id\", covariate_type=\"cluster_id\")
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CATEGORICAL(value:'categorical') -
NUMERIC(value:'numeric') -
EMBEDDING(value:'embedding') -
CLUSTER_ID(value:'cluster_id')