Exact Causal DAG Surgeries and Counterfactual Pearlian Bounds for Synthetic Anomaly Attribution
Isolating Generative Latent Inpainting via do-Calculus and Structural Causal Models
Addressing the confounding bias introduced by foundation model priors in deepfake detection, we construct a Pearlian Structural Causal Model (SCM). By executing do-calculus graph surgery, we isolate the direct causal effect of generative latent perturbations against physical sensor noise (PRNU, ELA, and rPPG), establishing defensible counterfactual attribution bounds.
Demonstrating do-calculus intervention and counterfactual isolation of generative artifacts.
Click any node in the causal diagram to inspect structural equation parameters.
1. Confounding in Generative Detection
Observed correlations between generative artifacts and image anomalies are frequently confounded by latent generative seeds, compression codecs, and camera sensor post-processing. A naive conditional probability P(Y | X) misattributes platform compression to synthetic manipulation.
By modeling the media genesis process as a directed acyclic graph (DAG), we explicitly separate synthetic inpainting interventions do(I = 1) from platform quantization noise.
2. Identifiability of Counterfactual Attribution
We state the structural equation model for the forensic sensor manifold. Applying Pearl’s back-door criterion, we identify the causal effect of generative synthesis on discrete frequency residuals.
Under non-parametric structural equation models with additive physical camera noise, the interventional distribution P(Y | do(X=x)) is uniquely identifiable from the observed joint distribution P(X, Y, Z) and verified PRNU calibration sets.
3. Judicial Admissibility Standards
Counterfactual isolation provides verifiable causal explanations that satisfy Daubert/Frye admissibility requirements, providing courts with definitive mathematical proofs of intervention rather than opaque confidence percentages.
@article{zal2026causal,
title={Exact Causal DAG Surgeries and Counterfactual Pearlian Bounds for Synthetic Anomaly Attribution},
author={Zal Logic Research Team},
journal={Zal Logic Technical Reports},
volume={2},
pages={1--18},
year={2026},
publisher={Zal Logic Inc.}
}