Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event # a parameter is either identified or not # and to view point identification as a precondition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how INDICE Missing Outcomes * Instrumental Variables * Conditional Prediction with Missing Data * Contaminated Outcomes * Regressions, Short and Long * Response-Based Sampling * Analysis of Treatment Response * Monotone Treatment Response * Monotone Instrumental Variables * The Mixing Problem