CIVIC-IT: A Causal–Invariant–Conformal Framework For Robust HR Analytics In Heterogeneous IT Environments
DOI:
https://doi.org/10.29284/b5pvk690Keywords:
HR analytics; causal inference; invariant learning; domain adaptation; conformal prediction; policy optimization.Abstract
Human–resource (HR) models often fail when deployed across heterogeneous organizational units because correlations are confounded by selection and degrade under environment shift. We present CIVIC-IT, a Causal–Invariant–Conformal framework that jointly targets identification, transfer, and calibrated uncertainty for HR decision making. First, we estimate average and heterogeneous effects of environment-level diversity levers using cross-fitted double/debiased machine learning with cluster-robust inference. Second, we learn shift-robust representa- tions via invariant risk minimization (IRM) combined with a domain-adversarial objective (DANN) and fine-tune with Group–DRO to control worst-environment risk. Third, we attach distribution-free guarantees using split-conformal predic- tion for continuous outcomes and label-conditional conformal predictive sets for
a “high T ∗” (turnover intention) flag. Outcomes are psychometrically grounded latent scores for Inclusion, Engagement, and T ∗ built from ordinal indicators with reliability weighting and multi-group diagnostics for approximate invari- ance. Using the IBM HR Attrition dataset (N =1,470) configured into realistic
environments (Department, JobRole, and Department×Role), we find: (i) global ATEs are small, but policy-relevant heterogeneity emerges—e.g., for early- career Research Scientist teams, greater diversity of education field increases Inclusion/Engagement, whereas highly heterogeneous business–travel patterns reduce them; (ii) leave-one-environment-out evaluation identifies stable trans- fer with clear best/worst environments and improved worst-group error under Group–DRO; (iii) conformal intervals achieve near-nominal coverage, and the risk classifier yields predominantly singleton predictive sets at the target error rate; and (iv) multi-objective policy search produces fairness-aware Pareto port- folios that deliver concurrent (albeit modest) gains in Inclusion and Engagement with slight reductions in T ∗. CIVIC-IToffers a governance-ready blueprint that links robust estimation to auditable, deployable HR policy in heterogeneous IT environments.
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