International trade is of vital importance for modern economies, and governments around the world try to shape their countries' exports and imports through numerous interventions. Given the problems facing trade negotiations through the World Trade Organization (WTO), countries have increasingly turned to preferential trade agreements (PTAs) involving only one or a small number of partners. At the same time, attention has shifted from reductions of import tariffs to the role of non-tariff barriers such as differences in regulations and technical standards. Accordingly, modern PTAs contain a host of provisions besides tariff reductions, in areas as diverse as services trade, competition policy or public procurement. A key question in international trade research is how to estimate the effects of PTAs and their individual provisions on trade flows. We argue that methods from the machine learning literature can help address this challenge, and that such methods are often superior to existing approaches. We use the term 'machine learning' to refer to algorithms used for statistical prediction that are trained on subsets of the available data to make forecasts of quantifiable outcomes (here: trade flows). While such algorithms have started to be applied in economic research, they have not been used for the analysis of PTAs nor in international economics more generally. First, machine learning can help evaluate the suitability of existing methods for estimating PTA effects. Such methods evaluate PTAs by comparing the trade flows observed after the implementation of an agreement to a so-called counterfactual outcome that shows what would have happened to trade flows in the absence of a PTA. This counterfactual is invariably based on a specific statistical model. Currently, by far the most common model is the so-called gravity equation. The estimated effect does of course depend on how well the gravity equation predicts counterfactual trade flows. We will use machine learning to develop a more flexible forecast to which we can compare the gravity equation's predictive power. Machine learning can also help improve existing methods for PTA evaluation. Implicitly, approaches based on the gravity equation construct a counterfactual by using an average of the changes in trade flows between countries not involved in a PTA. Similar approaches have been applied in a range of contexts besides international trade. Recent methodological advances have shown how these approaches can be improved by applying machine learning to select more complex combinations of control units (here: countries not participating in a PTA) than simple averages. Despite their potential, these techniques have not been applied in international trade research, and we propose to adapt them to this context. Finally, machine learning can be used to determine the relative importance of individual PTA provisions. The key challenge existing research has faced is that many PTAs contain similar provisions, making it difficult to estimate their effect on trade flows separately. Thus, researchers usually aggregate provisions in some way, for example by combining them into broad groups. This limits the relevance to policymakers who need to know if they should include a given individual provision in a PTA. This problem is reminiscent of the issue of 'feature selection' in machine learning where algorithms must decide which of many potentially relevant variables to include for forecasting purposes. We plan to use a subgroup of these methods that allow to identify the subset of variables (here: provisions) with the largest effect and to accurately estimate their impact. Overall, the proposed research will deepen our understanding of how PTAs impact trade flows. This, and the empirical techniques we plan to develop, will help researchers and policymakers involved in the design and evaluation of PTAs and ultimately contribute to a better, more evidence-based trade policy.