A MICE-Doubly Robust Causal Inference Pipeline for High-Dimensional Observational Data: Analyzing Productivity Effects of Digital Transformation in Korean Manufacturing 


Vol. 15,  No. 2, pp. 130-138, Feb.  2026
https://doi.org/10.3745/TKIPS.2026.15.2.130


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  Abstract

Causal inference in high-dimensional observational data requires addressing the dual challenges of missing data and model misspecification. We developed a pipeline that systematically integrates Multiple Imputation by Chained Equations (MICE) with the doubly robust Augmented Inverse Probability Weighting (AIPW) estimator to evaluate the productivity effects of digital transformation using data from the Korean Business Activity Survey (n=31,572, p=281, 2019–2023). By employing a MICE strategy that excludes the treatment variable, we increased the sample size by 76% (from 17,897 to 31,572), while K=5 cross-validation in AIPW estimation mitigated the risk of model misspecification. Our findings indicate that digital transformation yields an average total factor productivity (TFP) increase of 3.9%. This effect was particularly pronounced in the pre-pandemic period (+5.3%) and in the electronics (+8.6%) and chemical (+13.5%) industries. Robustness was confirmed through additional tests, including placebo tests and subsample analyses. Compared to Targeted Maximum Likelihood Estimation (TMLE), AIPW provided nearly identical estimates with a 27% faster computation time.

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[IEEE Style]

L. S. Min, "A MICE-Doubly Robust Causal Inference Pipeline for High-Dimensional Observational Data: Analyzing Productivity Effects of Digital Transformation in Korean Manufacturing," The Transactions of the Korea Information Processing Society, vol. 15, no. 2, pp. 130-138, 2026. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.130.

[ACM Style]

Lee Seog Min. 2026. A MICE-Doubly Robust Causal Inference Pipeline for High-Dimensional Observational Data: Analyzing Productivity Effects of Digital Transformation in Korean Manufacturing. The Transactions of the Korea Information Processing Society, 15, 2, (2026), 130-138. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.130.

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