Causal Effects of Digital Infrastructure and Agricultural Socialized Services on Agricultural Value Chain Efficiency: Identification Based on SFA-DML and Innovation-Driven Mechanisms

Digital infrastructure (DI) and agricultural socialized services (ASSs) are key drivers of agricultural value chain efficiency (AVCE), yet their causal effects remain confounded by endogeneity bias. This study constructs an integrated SFA-Tobit-DML framework using 31-province Chinese panel data (2011–2023). SFA estimates provincial technical efficiency; Tobit provides associational benchmarks; and Double Machine Learning (DML) with K = 5 cross-fitting identifies causal average treatment effects (ATEs). The DML results validate the Tobit benchmark for DI (LassoCV ATE: 0.810, 95% CI [0.656, 0.963]; Tobit: 0.826), while revealing Tobit overestimates ASSs by approximately 37% (LassoCV: 0.850 [0.599, 1.101]; Tobit: 1.347). Bootstrap mediation tests confirm that agricultural R&D investment mediates 61.58% and 47.45% of DI and ASS effects. Heterogeneity analysis shows that urbanization amplifies DI marginal returns (ATE: 1.401 in low-urban vs. 0.711 in high-urban regions), while government intervention exhibits conditional crowding-out effects. These findings imply that policies should shift from uniform investment toward regionally differentiated strategies that account for local absorptive capacity.