Counterfactual Sample

Counterfactual samples are synthetic data points representing what would have happened under different conditions, crucial for understanding causal relationships and improving model robustness. Current research focuses on generating high-quality counterfactuals using generative models like diffusion models and variational autoencoders, often incorporating causal reasoning and addressing challenges like high-dimensionality and time-varying treatments. These techniques are applied across diverse fields, including healthcare, fairness in AI, and improving the robustness of machine learning models by mitigating spurious correlations in training data, leading to more reliable and equitable decision-making systems.