Heckit model

The Heckit model is a statistical tool used to analyze situations where some data is missing or censored, especially when the missingness is related to the outcome itself. It combines two parts: the first models whether an observation is observed or missing (selection process), and the second predicts the outcome among the observed data. This approach helps avoid biased results caused by non-random missing data, providing more accurate estimates. Essentially, it corrects for biases introduced when the data collection process influences what data is available for analysis.