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Found 3 papers, 2 papers with code
Generalization bound for estimating causal effects from observational network data
To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM).
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e. g., marketing and medicine.
On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
Causal discovery from observational data is an important but challenging task in many scientific fields.
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