Causal Assumption

Causal assumption research focuses on establishing and leveraging assumptions about cause-and-effect relationships to draw reliable inferences from observational data, particularly in scenarios where randomized controlled trials are infeasible. Current research emphasizes developing methods for estimating heterogeneous treatment effects, often employing machine learning techniques like causal forests, rule learning algorithms, and deep adversarial networks to identify subgroups responding differently to interventions and to handle violations of standard causal assumptions such as unconfoundedness and overlap. This work is crucial for advancing fields like personalized medicine and policy evaluation by enabling more accurate and interpretable causal analyses from complex, real-world data.