Causal Identification of Artificial Intelligence Effects on Enterprise Labor Structure via a Partially Linear Double Machine Learning Estimator: Evidence from High-Dimensional Panel Data

This study develops a semiparametric causal inference framework to quantify the effect of Artificial Intelligence (AI) adoption on enterprise labor structure under high-dimensional confounding. We employ the Double Machine Learning (DML) estimator proposed, which combines Neyman orthogonality and cross-fitting to achieve reliable causal identification in settings where conventional regression methods are prone to bias from high-dimensional controls and nonlinear confounding. Nuisance functions are estimated using Lasso and Random Forests, enabling flexible modeling of complex relationships between control variables and outcomes. Using an unbalanced panel of Chinese A-share listed companies spanning 2006 to 2023, we identify a significant positive average treatment effect of AI adoption on the share of high-skilled labor (estimate: 0.118; 95% CI: [0.073, 0.163]), indicating that complementarity between AI and skilled workers dominates substitution at the firm level. Heterogeneity analysis reveals that the effect is stronger in manufacturing (0.183) than in services (0.071), and more pronounced in Eastern China (0.142) than in Central and Western regions (0.079). Quantile regression further shows that the complementarity effect intensifies at higher skill quantiles. A Panel Smooth Transition Regression (PSTR) model identifies a digitalization threshold beyond which AI–skill complementarity further strengthens. Mediation analysis confirms that productivity enhancement, digital transformation, and innovation activities together account for the majority of the total effect, with productivity improvement alone contributing approximately 34%. Placebo tests and propensity score weighting validate the robustness of our findings.