Propensity Score

Propensity score methods are statistical techniques used to estimate causal effects from observational data by adjusting for confounding variables—factors that influence both treatment assignment and the outcome of interest. Current research emphasizes improving the robustness and accuracy of propensity score estimation, particularly focusing on doubly robust estimators, inverse probability weighting, and methods that incorporate uncertainty quantification and calibration. These advancements are crucial for reliable causal inference across diverse fields, ranging from healthcare and economics to online advertising and social media analysis, enabling more accurate evaluation of interventions and policies.