Conditional Moment Restriction

Conditional moment restrictions (CMR) provide a framework for estimating relationships in data where direct observation is impossible or unreliable, focusing on identifying parameters that satisfy specified conditional moment equations. Current research emphasizes developing robust and efficient estimation methods, including stochastic generalized method of moments (SGMM) for large-scale and streaming data, kernel methods of moments (KMM) that move beyond simple data reweighting, and neural network-based approaches for improved flexibility and scalability. These advancements are significantly impacting causal inference, econometrics, and machine learning by enabling more accurate and reliable estimation in complex settings with high-dimensional data and potential confounding factors.