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

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! 

This work falls into three major categories:

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

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

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.

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. 

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

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

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