I am an econometrician with research interests in causal inference and weak identification. I primarily focus on developing new methods for causal inference in a more realistic setting of treatment effects heterogeneity. I have also contributed research on weak identification with many instruments. Currently I am a Lecturer in the Department of Economics at University College London and an Untenured Associate Professor (on leave) at CEMFI. Previously I was a Postdoctoral Research Fellow at UC Berkeley. I received my Ph.D in Economics and Statistics from MIT in 2021, and my B.A. in Economics and Mathematics from Wellesley College in 2014.
Linear instrumental variable regressions are widely used to estimate causal effects. Many instruments arise from the use of “technical” instruments and more recently from the empirical strategy of “judge design”. This paper surveys and summarizes ideas from recent literature on estimation and statistical inferences with many instruments. We discuss how to assess the strength of the instruments and how to conduct weak identification-robust inference under heteroscedasticity. We establish new results for a jack-knifed version of the Lagrange Multiplier (LM) test statistic. Many exogenous regressors arise often in practice to ensure the validity of the instruments. We extend the weak-identification-robust tests to settings with both many exogenous regressors and many instruments. We propose a test that properly partials out many exogenous regressors while preserving the re-centering property of the jack-knife. The proposed tests have uniformly correct size and good power properties.