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Chad is an assistant professor of political science and statistics at UCLA. His methodological research focuses mainly on methods for making credible and transparent causal claims from “imperfect” research designs, such as where a randomized trial is not desirable or possible. This includes practical methods of sensitivity analysis that allow us to produce and judge research that does not make a defensible assumption of “zero confounding bias”. It also includes research designs that can make credible inferences under partial randomization, or under complete self-selection into treatment, while employing transparent assumptions. He also works on measurement of sensitive attitudes, such as ethnic biases, through procedures borrowed from social psychology. In his applied work, Chad has often focused on civil war and violence, particularly estimating understanding the effects of exposure to violence on attitudes. More recently he is focusing on policy oriented causal questions, such as determining the effects of eviction on homelessness in the US, and medical evaluations such as assessing the effects of newly promoted treatments for Sepsis and tuberculosis in contexts where randomization is not possible or ethical. Chad has worked in India, Chad, Ethiopia, and Kenya. At UCLA, he teaches courses on causal inference, statistics, and machine learning. Prior to joining UCLA, Chad earned a PhD in Political Science from MIT, and spend a pre-doctoral year at Princeton.

Position: Assistant Professor

Institution / Affiliation : UCLA Departments of Statistics and Political Science

Geographical Region: Africa, North America
Methodology: Experimental Design, Field Experiments, Lab Experiments, Statistics, Survey Methodology
Policy: Conflict and Violence, Development