I am an associate professor at UCLA in Statistics and 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" causal inference: developing research methods that enable researchers across disciplines to more feasibly make credible causal inferences from the available data and assumptions.
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.
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.
Together with Onyebuchi Arah and a cadre of impressive students, we will soon be launching the Practical Causal Inference lab. This will become a home for much of our ongoing work on the practical application of causal inference in difficult settings, including the social sciences as well as the medical sciences.
I teach courses on statistics, causal inference, and machine learning. Occasionally I also teach about civil conflict and mass atrocity.
You can reach me at chazlett at UCLA dot edu.