Nonparametric Instrumental Variable

Nonparametric instrumental variable (NPIV) methods address causal inference challenges when relationships between variables are complex and unobserved confounders exist, aiming to accurately estimate causal effects despite these complexities. Current research focuses on developing efficient algorithms, such as stochastic gradient descent and primal-dual optimization, often employing flexible model architectures like neural networks or kernel methods to handle diverse data types and non-linear relationships. These advancements improve the accuracy and robustness of causal effect estimation in various applications, including dynamic pricing, off-policy evaluation in reinforcement learning, and treatment effect analysis with measurement error, impacting fields like economics, marketing, and healthcare.