I am a professor at UCLA in the Department of Statistics and Data Science and in the Department of Political Science.

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A central question facing scientists in almost every field is: How do we know if one thing causes another, particularly if we cannot run a randomized experiment? Further, many important questions cannot be answered even with a randomized experiment.  My methodological work focuses on "feasible" or "practical" causal inference: developing research methods that enable researchers across disciplines to more feasibly make credible causal inferences from the available data and assumptions. Visit the Practical Causal Inference lab to learn more! 

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This work falls into three major categories:

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1. New identification strategies that allow us to make causal claims based on assumptions that are more realistic--or at least more understandable and susceptible to debate--than those of existing approaches

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2. Sensitivity analyses that explore how violating important assumptions (such as unconfoundedness or the exclusion restriction) changes our inferences; and 

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3. Estimation and modeling:  statistical estimation problems related to causal inference, such as weighting estimators, employing kernels and other tools from machine learning to relax functional form concerns, and time-series analysis.

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Meanwhile, much of my substantive work has focused on civil war, indiscriminate violence, and mass atrocity. In recent years I have also been increasingly involved in medical sciences and other fields where my methodological tools can add value. I also still work on the occasional neuroscience work, an earlier focus of mine. 

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I teach courses on statistics, causal inference, and machine learning. 

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You can reach me at chazlett at stat dot UCLA dot edu.