Data Engineering and Causal Inference

Scholars of International Relations (IR) can benefit enormously from learning the fundamentals of data engineering, including how to use cloud computing platforms. This guide offers a practical introduction to these tools, designed primarily for IR graduate students but accessible to anyone interested in expanding their methodological skills. It is intended as a quick-start guide, not a comprehensive course: the objective is to provide enough familiarity with the available tools to begin using them independently.

Some of the most compelling research in political science relies on novel datasets. Yet constructing these datasets is often considerably more labor-intensive than analyzing them. Researchers may spend months collecting information, reconciling inconsistent sources, cleaning observations, and maintaining databases before they can even begin testing their hypotheses. In practice, much of empirical social science involves data engineering, not merely data analysis. Despite this, methodological training overwhelmingly emphasizes the latter.

Data engineering can substantially reduce this burden. Rather than manually collecting, cleaning, and organizing information, researchers can build automated pipelines that perform these tasks repeatedly and reliably. Consider a project using social media data: a well-designed pipeline could make thousands of API requests, clean and standardize the resulting observations, store them in a database, and update the analysis as new information becomes available. What might otherwise require extensive manual work can instead operate as a reproducible, largely automated system.

Building such systems does not necessarily require expensive hardware or a dedicated technical team. Cloud computing providers, such as Amazon Web Services (AWS) and Google Cloud, offer access to computational resources, databases, storage, and automation tools on demand. Researchers can therefore use sophisticated infrastructure without purchasing or maintaining it themselves. Many applications can also be built with relatively modest computing resources and costs.

The principal obstacle is often knowing where to begin. Cloud platforms offer an enormous range of services, each with its own terminology, configuration requirements, and learning curve. For social scientists without a background in computer science, identifying which tools are useful—and which can safely be ignored—can be challenging.

This guide is designed to lower that barrier to entry. It provides a streamlined collection of introductory videos covering some of the foundational tools and concepts needed to get started with data engineering and cloud computing. It does not attempt to cover the subject comprehensively. Instead, it aims to equip IR scholars with sufficient practical knowledge to begin experimenting, building basic data pipelines, and exploring more advanced capabilities as their research demands.

The underlying argument is straightforward: social scientists should learn not only how to analyze data, but also how to engineer the systems that produce it. Doing so can make ambitious empirical projects more feasible, reduce repetitive research labor, and open up questions that would otherwise be difficult to investigate.