Synthetic Causal

Synthetic causal inference focuses on generating artificial datasets to advance causal discovery and effect estimation methods. Current research emphasizes developing complex, realistic synthetic datasets that incorporate features like selection bias and confounding, and evaluating the performance of various causal inference algorithms, including Bayesian approaches and neural network architectures like Dragonnet and its variants, on these datasets. This work is crucial for validating and improving causal inference techniques, ultimately leading to more reliable causal conclusions in diverse scientific fields and practical applications where randomized controlled trials are infeasible.