University of Chicago Booth School of Business
5807 South Woodlawn Avenue
Chicago, IL 60637
Institutional Affiliation: University of Chicago
Information about this author at RePEc
NBER Working Papers and Publications
|April 2018||Pre-event Trends in the Panel Event-study Design|
with Simon Freyaldenhoven, Jesse M. Shapiro: w24565
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends ("pre-trends") in the outcome. Alternative approaches perform poorly in our simulations.
Published: Simon Freyaldenhoven & Christian Hansen & Jesse M. Shapiro, 2019. "Pre-Event Trends in the Panel Event-Study Design," American Economic Review, vol 109(9), pages 3307-3338. citation courtesy of
|June 2017||Double/Debiased Machine Learning for Treatment and Structural Parameters|
with Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Whitney Newey, James Robins: w23564
We revisit the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0. We depart from the classical setting by allowing for η_0 to be so high-dimensional that the traditional assumptions, such as Donsker properties, that limit complexity of the parameter space for this object break down. To estimate η_0, we consider the use of statistical or machine learning (ML) methods which are particularly well-suited to estimation in modern, very high-dimensional cases. ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice. However, both regularization bias and overfitting in estimating η_0 cause a heavy bias in estimators of θ_0 that are...
Published: Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," The Econometrics Journal, vol 21(1), pages C1-C68. citation courtesy of