Pseudo Likelihood

Pseudo-likelihood methods offer computationally efficient alternatives to traditional likelihood-based approaches, particularly when dealing with intractable likelihood functions in complex models. Current research focuses on applying pseudo-likelihood to diverse areas, including causal inference (e.g., estimating heterogeneous treatment effects), graphical model learning (e.g., community detection), and machine learning tasks (e.g., membership inference attacks and quantile regression). This versatility makes pseudo-likelihood a valuable tool for addressing challenges in high-dimensional data analysis and model estimation across various scientific disciplines, improving both the efficiency and robustness of inference.