Article: Identification of new quality productivity levels in agricultural digital transformation using causal inference from confounded data Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.96 pp.20 - 43 Abstract: In the process of agricultural digital transformation, field sensor and remote sensing data are often mixed with interfering factors such as soil heterogeneity and management differences, disconnecting the causal chain between digital investment and new quality productivity. This paper proposes a dual causal inference framework, the digital transformation driven new quality productive forces analysis framework, integrating propensity score weighting and double machine learning to isolate the net effect of digitalisation from mixed data. Experiments on two public datasets with over 67,000 samples show that adopting digitalisation increases the new quality productivity index by 21.6% (95% confidence interval 17.8%-25.4%). The framework improves causal entropy from 0.42 to 0.67 and reduces neighbourhood confounding degree from 0.31 to 0.13, indicating a residual confounding reduction of approximately 40%-58%. This method fills the causal attribution gap and provides a quantifiable path for distinguishing correlation from causation. Inderscience Publishers - linking academia, business and industry through research

Title: Identification of new quality productivity levels in agricultural digital transformation using causal inference from confounded data

Authors: Yanhong Wang; Mingyue Zhang; Junqing Sheng

Addresses: College of Economics and Management, Shandong Huayu University of Technology, Dezhou, 253034, China ' College of Economics and Management, Shandong Agricultural University, Taian, 271018, China ' College of Economics and Management, Shandong Huayu University of Technology, Dezhou, 253034, China

Abstract: In the process of agricultural digital transformation, field sensor and remote sensing data are often mixed with interfering factors such as soil heterogeneity and management differences, disconnecting the causal chain between digital investment and new quality productivity. This paper proposes a dual causal inference framework, the digital transformation driven new quality productive forces analysis framework, integrating propensity score weighting and double machine learning to isolate the net effect of digitalisation from mixed data. Experiments on two public datasets with over 67,000 samples show that adopting digitalisation increases the new quality productivity index by 21.6% (95% confidence interval 17.8%-25.4%). The framework improves causal entropy from 0.42 to 0.67 and reduces neighbourhood confounding degree from 0.31 to 0.13, indicating a residual confounding reduction of approximately 40%-58%. This method fills the causal attribution gap and provides a quantifiable path for distinguishing correlation from causation.

Keywords: causal inference; digital agriculture; new quality productive forces; double machine learning; DML.

DOI: 10.1504/IJICT.2026.156192

International Journal of Information and Communication Technology, 2026 Vol.27 No.96, pp.20 - 43

Received: 13 May 2026
Accepted: 18 Jun 2026
Published online: 07 Sep 2026 *

Title: Identification of new quality productivity levels in agricultural digital transformation using causal inference from confounded data

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