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.


UCL Email


I am also on Google Scholar. For the NBER Summer Institute Methods Lectures 2023 “Linear Panel Event Studies” (with Jesse M. Shapiro), the notes and recordings are available here.

Working Papers

“Weak Identification with Many Instruments” (August 2023), with Anna Mikusheva

Stata package.

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.

"Adapting to Misspecification" (June 2023), with Tim B. Armstrong and Patrick Kline, revision requested at Econometrica

Software, Video from the Chamberlain Seminar.

Empirical research typically involves an efficiency-robustness tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased if the assumptions are violated, or they can relax some of these assumptions to motivate a more variable unrestricted estimator that is asymptotically unbiased. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound is not known, or when that bound may not be sharp, we propose shrinkage estimators that are adaptive: they minimize the percentage increase in worst case risk relative to an oracle that knows the magnitude of the restricted estimator’s bias. We show how to compute the adaptive estimator by solving for a least favorable prior in a weighted convex minimax problem. A simple lookup table is provided for computing the adaptive estimates from the restricted and unrestricted estimates, their standard errors, and their correlation. We revisit five influential empirical papers and study how estimates of economic parameters change when adapting to misspecification.

“Empirical Welfare Maximization with Constraints"


“Temporal Aggregation for the Synthetic Control Method” (with Eli Ben-Michael and Avi Feller), AEA Papers and Proceedings, 2024 114: 614-17.

Preprint, Slides, Replication code.

“Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics” (with Rahul Singh), Econometrics Journal, 2024 27:1, 1-20.

Preprint, Supplementary Appendix

“A linear panel model with heterogeneous coefficients and variation in exposure” (with Jesse M. Shapiro), Journal of Economic Perspectives, 2022 36:4, 193-204.

Preprint, NBER Working Paper #29976, Slides, Video series.

“Inference with Many Weak Instruments” (with Anna Mikusheva), The Review of Economic Studies. 2022 89:5, 2663–2686.

Preprint, Supplementary Appendix, Replication code.

“Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects” (with Sarah Abraham), Journal of Econometrics. 2021 225:2, 175-199.

Preprint, Replication code, Slides.

eventstudyinteract is a Stata module that implements the interaction weighted estimator for an event study. Sun and Abraham (2021) proves that this estimator is consistent for the average dynamic effect at a given relative time even under heterogeneous treatment effects. eventstudyweights is a Stata module that estimate weights underlying two-way fixed effects regressions based on Sun and Abraham (2021).

“Weak Instruments in IV Regression: Theory and Practice” (with Isaiah Andrews and James Stock), Annual Review of Economics. 2019 11:1, 727-753.

Preprint, Appendix, Replication code.

“Implementing valid two-step identification-robust confidence sets for linear instrumental-variables models” Stata Journal 18(4), 803–825.

twostepweakiv is a Stata module that implements the two-step weak-instrument-robust confidence sets based on Andrews (2018) and the refined projection method for subvector inference based on Chaudhuri and Zivot (2011) for linear instrumental-variable (IV) models. Development versions and replication code for the article are available on GitHub.

Policy works

“Structural Reforms and Economic Growth: A Machine Learning Approach” (with Anil Ari and Gabor Pula)

IMF Working Paper No. 2022/184

Presentation: IMF European Department Seminar

Public Goods

Four lecture videos and slides on “Linear Panel Event Studies” (with Jesse M. Shapiro)

NBER Summer Institute Methods Lectures 2023


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