This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods minimize the risk of arbitrary and ad hoc model selection. Second, they provide a standardized way to trade off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.