Chad Hazlett is an associate professor at UCLA in Statistics and Political Science. His methodological work focuses on feasible causal inference: developing research methods that enable researchers across disciplines to more feasibly make credible causal inferences from observational data. 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. Chad also works on measurement of sensitive attitudes, such as ethnic biases, through procedures borrowed from social psychology. Much of his substantive work has focused on civil war, indiscriminate violence, and mass atrocity. In recent years he has also been increasingly involved in medical sciences and other fields where his methodological tools can add value. 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.
Chad Hazlett is an associate professor at UCLA in Statistics and Political Science. His methodological work focuses on feasible causal inference: developing research methods that enable researchers across disciplines to more feasibly make credible causal inferences from observational data. 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. Chad also works on measurement of sensitive attitudes, such as ethnic biases, through procedures…