This course introduces students to recent developments in the scholarly effort to derive causal explanations using quantitative methods. The bulk of the course will be concerned with how to identify and estimate causal effects in observational studies. It can be argued that this effort amounts to a paradigm shift within quantitative social science away from regression models and explained variance to identification and measurement of causal effects. Taking the randomized experiment as the ideal, we clarify the challenges faced by social scientists seeking to draw causal inferences from observational data. Units in observational studies usually select into their causal status (their ”treatment” status) through processes outside of the control of the researcher rather than being assigned to these causal states by the researcher, such as in controlled experiments. The characteristics of this selection process are central throughout the course. We present a range of approaches for identifying its’ core features and for drawing valid causal inferences given those features. In the process, we highlight the limitations and difficulties associated with causal estimates obtained via the different techniques. More than anything, the course aims to develop a critical, yet constructive, mindset towards claims of causal effects in observational studies The course will also give a brief introduction to basic techniques and concepts used for prediction purposes, and discuss how prediction differs from, and relates to, causal explanation. Together, causal inference and prediction constitute the two main activities of contemporary quantitative social science, and students of this course will be familiarized with the challenges and promises of both of them. Former title (before spring 2017): STV4027 - Causal Inference.