Miles D. Williams

Ph.D. Student, University of Illinois at Urbana-Champaign

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I am a Ph.D. student in political science at the University of Illinois at Urbana-Champaign, and I also serve as a methods specialist for the Office of Evaluation Sciences, housed in the U.S. General Services Administration in Washington, D.C. My primary research agenda focuses on rivalry and collective action problems that arise among industrialized countries in the allocation of foreign aid. I further have several secondary research projects that include the study of differential foreign aid responses by wealthy countries in the context of ongoing civil wars in developing countries, the outward-facing media strategy of emerging or non-traditional foreign aid donors like China, links between aid recipient governance and aid fragmentation, and international migration as both determinant and consequence of international aid. I also have an interest in causal inference in the context of observational data. For one project, I study the potential application of popular “black box” machine learners, like random forest regression, to the estimation average treatment effects. I additionally have contributed to text-as-data projects that rely on original data sources.

Research Interests

Recent Publications

Gender in the Pulpit: The Differences in Speaking Style for Men and Women (2019). With Ryan Burge

Is Social Media a Digital Pulpit? How Evangelical Leaders Use Twitter to Encourage the Faithful and Publicize Their Work (2019). With Ryan Burge

Working Papers

Targeting Civil War: Foreign Aid and the Opportunity Cost of Intra-State Violence

Stopping Spillovers or Seizing Strategic Advantage: Industrialized Country Interactions in Allocating Resources to the Developing World

Xinhua Coverage of Chinese Foreign Aid Allocations

After Paris, Busan! Progress on Bilateral Aid Fragmentation Afterall?

Fragmented Aid and Recipient Corruption: Diversity of Aid Partners and Corrupt Recipient Motives

Leveraging the “Black Box”: Random Forest Adjustment for Causal Inference

Other Writings

I occasionally have contributed to Religion in Public


In conjunction with my project on random forest adjustment, I’ve developed an R package called “RFA” that provides easy-to-use tools for implementing the method. The latest version can be found here.

I further am developing an R package with tools necessary to estimate a “Strategic Autoregressive Model,” useful for estimating free-riding or competitive behavior among a set of actors. The method is based on the work of Martin Steinwand—his paper describing the method in Political Analysis can be found here. The “SARM” package that I have developed estimates a modified version of the model proposed by Steinwand. The latest version of the package and additional details can be found here.