Yuya Sasaki
Brian and Charlotte Grove Chair
& Professor of Economics,
Editor-in-Chief: Econometric Reviews
yuya.sasaki[at]vanderbilt.edu
Ms. Jiatong Li is still on the 2024-2025 job market. Please feel free to contact me regarding this outstanding candidate.
● Academic Genealogy: Yuya Sasaki (2012, Brown) ⇒ Frank Kleibergen (1996, Erasmus) ⇒ Herman Koene van Dijk (1984, Erasmus) ⇒ Teunis Kloek (1966, Erasmus) ⇒ Henri Theil (1951, UvA) ⇒ Pieter Hennipman (1940, UvA) ⇒ Herman Frijda (1914, Leiden) ⇒ Hendrik Barend Greven (1875, Leiden) ⇒ Simon Vissering (1842, Leiden) ⇒ Cornelis Jacobus van Assen (1810, Leiden) ⇒ Hendrik Willem Tydeman (1799, Leiden) ⇒ Dionysius Godefridus van der Keessel (1761, Leiden) ⇒ Gerlach Scheltinga (1731, Franeker) ⇒ Abraham Wieling (1721, Marburg) ⇒ Johann Friedrich Hombergk zu Vach (1698, Utrecht) ⇒ Joannes Georgius Graevius (b. 1632, Leiden) ⇒ Daniël Heinsius (~1598, Leiden) ⇒ Joseph Justus Scaliger (b. 1540, Paris) ⇒ Adrianus Turnebus (b. 1512, Paris)
● Biological Genealogy: Yuya Sasaki (Showa 54 = 1979, Tokyo) ⇒ Shizuo Sasaki (Showa 25 = 1950, Akita) ⇒ Tadao Sasaki (Taisho 13 = 1926, Akita) ⇒ Rikichi Sasaki (Meiji 29 = 1893, Akita) ⇒ Jinkichi Sasaki (Meiji 1 = Keio 4 = 1868, Akita) ⇒ Torakichi Sasaki (Tenpo 13 = 1841, Akita) ⇒ Koichi Sasaki (Bunsei 9 = 1826, Akita) ⇒ Chojuro Sasaki (Kansei, Akita)
● NEWS
2025: Congratulations, Jooyoung Cha, on her appointment as Assistant Professor of Economics at the University of Notre Dame!
2024: I am assuming a new role as the editor-in-chief of Econometric Reviews starting August 1, 2024.
2024: Congratulations, Yukun Ma, on her appointment as Assistant Professor of Economics at the University of Rochester!
2021: Cluster robust double machine learning package now available in R and Python - thanks to Malte S. Kurz.
2020: Congratulations, Harold D. Chiang, on his appointment as Assistant Professor of Economics at the University of Wisconsin-Madison!
● Frequently Asked Questions about the Stata Command "robustate":
Q1. How does the "robustate" command compare with the existing IPW estimator such as the "teffects ipw" command?
A. "teffects ipw" tends to produce larger standard errors than "robustate". If the overlap is severely limited (i.e., if the tail index of the inverse propensity score is above 0.5), then the standard error for "teffects ipw" is not guaranteed to exist while that of "robustate" still exists.
Q2. How does the "robustate" command compare with the IPW estimation with trimming/truncating small propensity scores?
A. Trimmed and truncated estimators are biased for the average treatment effects (ATE), while the "robustate" estimator is de-biased and its standard error accounts for the effects of the de-biasing.
Q3. How does the "robustate" command compare with the matching estimators such as "teffects pamatch" and "teffects nnmatch" commands?
A. The matching estimators tend to be biased for the average treatment effects (ATE) when the overlap is limited, while the "robustate" estimator being de-biased consistently estimates the ATE and its standard error accounts for the effects of the de-biasing.
Q4. How does the "robustate" command compare with the overlap weighting approaches?
A. The "robustate" estimates the average treatment effects (ATE), while the overlap weighting approaches estimate only weighted averages of treatment effects and hence in general fail to estimate the ATE.