Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks

arXiv stat.ML5d4 min read

arXiv:2607.22313v1 Announce Type: new Abstract: Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence. This paper proposes SEM-DNN, a heteroscedastic neural simultaneous-equation estimator that learns reciprocal structural interactions without external instruments. Identification exploits conditional covariance diagonalization: when structural shocks have zero conditional means, are conditionally uncorrelated given predetermined covariates, a

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Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks

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