I am an assistant professor at UCLA in Statistics and Political Science. My work focuses on developing research methods that enable researchers across disciplines to more feasibly make credible causal inferences from observational data. This work falls under three major branches:

  1. Developing 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. "Old fashioned" work on 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.

My group is working feverishly to apply these tools to improve the response to COVID-19, especially using new identification strategies that allow us to learn the effects of unproven COVID-19 treatments like hydroxychloroquine and Remdesivir from those who are taking these treatments outside of randomized trials. If you have relevant interests or data, please contact me.

I teach courses on research design, causal inference, and machine learning.  I also have substantive interests in civil war, mass atrocity, and other forms of large scale violence, and in homelessness. 

Please see my ResearchGate page for information on the Feasible Causal Inference Lab as well as ongoing work on assessing the effects of COVID-19 therapies. More generally you can find working papers and publications on my Research page. You can reach me at chazlett at UCLA dot edu.

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