When evaluating the impact of a certain policy or intervention, one of the main challenges is to determine what would have happened to the beneficiaries if the program had not existed (counterfactual). Since that is logically impossible to observe, researchers usually try to find a convincing comparison group. Randomization is typically considered the ideal way of measuring impact. However it is not always possible for researchers to carry out random trials. In this capacity building session, we will go over some basic concepts related to impact evaluation as well as some second-best alternatives to randomization, namely: Difference-in-Differences, Propensity Score Matching, Regression Discontinuity Design and Instrumental Variables. The seminar does not aim to study each of these alternatives in depth but rather to briefly present each of them and their basic workings so that researchers become familiar with some of the options available to them.