Fix selection bias with automated feature assignment for Heckman models

Fix selection bias with automated feature assignment for Heckman models targets a $6.0B = 200,000 mid+ enterprises x $30K ACV total addressable market with medium saturation and a year-over-year growth rate of 18% (enterprise analytics & AutoML adoption growth).

Key trends driving demand: Causal ML adoption -- businesses seek models that estimate causal effects and correct for selection to make reliable decisions.; Regulatory scrutiny on model bias -- requires explainable bias-correction and audit trails for high-risk domains.; AutoML & MLOps maturity -- makes it easier to deploy econometric-correction steps into production pipelines.; Pretrained LLMs for code/metadata -- enable automated mapping of dataset fields to econometric roles at scale..