Amortized transformer model with conditional fixed-point iterations learns SCM causal mechanisms from data and graphs, matching per-dataset baselines and outperforming in low-data regimes.
Deep end-to-end causal inference.arXiv preprint arXiv:2202.02195,
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RetiSEM is a domain-constrained SEM framework that improves causal graph recovery and mediation analysis on fragmented biomedical data via biologically informed blocks and forbidden-edge constraints, outperforming unconstrained baselines on synthetic benchmarks.
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Amortized Inference of Causal Models via Conditional Fixed-Point Iterations
Amortized transformer model with conditional fixed-point iterations learns SCM causal mechanisms from data and graphs, matching per-dataset baselines and outperforming in low-data regimes.
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RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
RetiSEM is a domain-constrained SEM framework that improves causal graph recovery and mediation analysis on fragmented biomedical data via biologically informed blocks and forbidden-edge constraints, outperforming unconstrained baselines on synthetic benchmarks.