Disentangling Doubt in Deep Causal AI

• URL: http://arxiv.org/abs/2507.03622v1
• Date: Fri, 04 Jul 2025 14:48:51 GMT
• Title: Disentangling Doubt in Deep Causal AI
• Authors: Cooper Doyle,
• Abstract summary: We propose a factorized Monte Carlo Dropout framework for deep twin-network models that splits total predictive variance into representation uncertainty.<n>Across three co-shift regimes, our intervals are well-calibrated and satisfy sigma_rep2 + sigma_pred2 sigma_tot2.<n>This module-level decomposition offers a practical diagnostic for detecting and interpreting uncertainty sources in deep causal-effect models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate individual treatment-effect estimation in high-stakes applications demands both reliable point predictions and interpretable uncertainty quantification. We propose a factorized Monte Carlo Dropout framework for deep twin-network models that splits total predictive variance into representation uncertainty (sigma_rep) in the shared encoder and prediction uncertainty (sigma_pred) in the outcome heads. Across three synthetic covariate-shift regimes, our intervals are well-calibrated (ECE < 0.03) and satisfy sigma_rep^2 + sigma_pred^2 ~ sigma_tot^2. Additionally, we observe a crossover: head uncertainty leads on in-distribution data, but representation uncertainty dominates under shift. Finally, on a real-world twins cohort with induced multivariate shifts, only sigma_rep spikes on out-of-distribution samples (delta sigma ~ 0.0002) and becomes the primary error predictor (rho_rep <= 0.89), while sigma_pred remains flat. This module-level decomposition offers a practical diagnostic for detecting and interpreting uncertainty sources in deep causal-effect models.

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